Experimental Studies on Intelligent, Wearable and Automated Wireless Mobile Tele-Alert System for Continuous Cardiac Surveillance
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Safari, A. Ahmadian, M.A.A. Golkar" "autores" => array:3 [ 0 => array:2 [ "nombre" => "A." "apellidos" => "Safari" ] 1 => array:2 [ "nombre" => "A." "apellidos" => "Ahmadian" ] 2 => array:2 [ "nombre" => "M.A.A." "apellidos" => "Golkar" ] ] ] ] ] "idiomaDefecto" => "en" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S1665642313715232?idApp=UINPBA00004N" "url" => "/16656423/0000001100000001/v2_201505081621/S1665642313715232/v2_201505081621/en/main.assets" ] "itemAnterior" => array:18 [ "pii" => "S1665642313715219" "issn" => "16656423" "doi" => "10.1016/S1665-6423(13)71521-9" "estado" => "S300" "fechaPublicacion" => "2013-02-01" "aid" => "71521" "copyright" => "Universidad Nacional Autónoma de México" "documento" => "article" "licencia" => "http://creativecommons.org/licenses/by-nc-nd/4.0/" "subdocumento" => "fla" "cita" => "Journal of Applied Research and Technology. 2013;11:124-32" "abierto" => array:3 [ "ES" => true "ES2" => true "LATM" => true ] "gratuito" => true "lecturas" => array:2 [ "total" => 2545 "formatos" => array:3 [ "EPUB" => 36 "HTML" => 1674 "PDF" => 835 ] ] "en" => array:11 [ "idiomaDefecto" => true "titulo" => "Elimination of Harmonics in Multilevel Inverters Connected to Solar Photovoltaic Systems Using ANFIS: An Experimental Case Study" "tienePdf" => "en" "tieneTextoCompleto" => "en" "tieneResumen" => "en" "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "124" "paginaFinal" => "132" ] ] "contieneResumen" => array:1 [ "en" => true ] "contieneTextoCompleto" => array:1 [ "en" => true ] "contienePdf" => array:1 [ "en" => true ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "f0040" "etiqueta" => "Figure 8" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr8.jpeg" "Alto" => 615 "Ancho" => 888 "Tamanyo" => 75979 ] ] "descripcion" => array:1 [ "en" => "<p id="sp0040" class="elsevierStyleSimplePara elsevierViewall">Results of the case study.</p>" ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "T.R. Sumithira, A. Nirmal Kumar" "autores" => array:2 [ 0 => array:2 [ "nombre" => "T.R." "apellidos" => "Sumithira" ] 1 => array:2 [ "nombre" => "A." "apellidos" => "Nirmal Kumar" ] ] ] ] ] "idiomaDefecto" => "en" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S1665642313715219?idApp=UINPBA00004N" "url" => "/16656423/0000001100000001/v2_201505081621/S1665642313715219/v2_201505081621/en/main.assets" ] "en" => array:16 [ "idiomaDefecto" => true "titulo" => "Experimental Studies on Intelligent, Wearable and Automated Wireless Mobile Tele-Alert System for Continuous Cardiac Surveillance" "tieneTextoCompleto" => true "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "133" "paginaFinal" => "143" ] ] "autores" => array:1 [ 0 => array:3 [ "autoresLista" => "S. Palanivel Rajan, R. Sukanesh" "autores" => array:2 [ 0 => array:4 [ "nombre" => "S." "apellidos" => "Palanivel Rajan" "email" => array:1 [ 0 => "palanivelrajanme@gmail.com" ] "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">1</span>" "identificador" => "aff0005" ] ] ] 1 => array:3 [ "nombre" => "R." "apellidos" => "Sukanesh" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">2</span>" "identificador" => "aff0010" ] ] ] ] "afiliaciones" => array:2 [ 0 => array:3 [ "entidad" => "Assistant Professor, Department of Electronics and Communication Engineering, Kamaraj College of Engineering and Technology, Virudhunagar, Tamilnadu, India" "etiqueta" => "1" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Professor, Department of Electronics and Communication Engineering, Thiagarajar College of Engineering, Madurai, Tamilnadu, India" "etiqueta" => "2" "identificador" => "aff0010" ] ] ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "f0015" "etiqueta" => "Figure 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 788 "Ancho" => 1716 "Tamanyo" => 143058 ] ] "descripcion" => array:1 [ "en" => "<p id="sp0015" class="elsevierStyleSimplePara elsevierViewall">LabVIEW design for of ECG noise analysis.</p>" ] ] ] "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">1</span><span class="elsevierStyleSectionTitle" id="sect0015">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">According to a recent survey by WHO (World Health Organization), myocardial malfunctioning is the major cause for sudden cardiac deaths all over the world <a class="elsevierStyleCrossRef" href="#bib0040">[8]</a>. There are about 5 lakhs numbers of deaths in India every year and the number runs to millions worldwide <a class="elsevierStyleCrossRef" href="#bib0050">[10]</a>. Each year the American Heart Association (AHA), National Institute of Health takes the survey to update the statistics on death due to heart diseases, strokes and other vascular diseases <a class="elsevierStyleCrossRef" href="#bib0030">[6]</a>. It would be useful to take preventive actions based on the survey to safeguard lives of such patients at least for the future <a class="elsevierStyleCrossRef" href="#bib0080">[16]</a>. In 2007 the overall death rate from Cardio Vascular Disease (CVD) was 251.2 per 100000 <a class="elsevierStyleCrossRef" href="#bib0060">[12]</a>. By 2007, the death rate had increased to 27.8% according to the last survey. Based on this survey, now we know that 2200 people die each day at an average of 1 death for every 39 seconds <a class="elsevierStyleCrossRef" href="#bib0055">[11]</a>. The 2011 survey also shows that the lack of a proper diet (obesity) is the major cause for cardiac attacks <a class="elsevierStyleCrossRef" href="#bib0045">[9]</a>. The elderly groups are largely affected by this problem. Among children from 2 to19 years old, 31.9% are fat and obese <a class="elsevierStyleCrossRef" href="#bib0010">[2]</a>. Among people in the range of 20 years old and above, 67.37% are plump and obese <a class="elsevierStyleCrossRef" href="#bib0020">[4]</a>.</p><p id="par0010" class="elsevierStylePara elsevierViewall">Our aim in this paper, is to create an equipment that allow us to detect the cardiac disorder in an early (initial) stage. This proposed model will be mainly useful for the elderly population with heart rate variability and the patients under critical condition. The goal is to detect the cardiac disorder earlier consequently t the patient will be given first-order medical attention within the first few critical hours, which would greatly improve their chance of survival. The device acquires the ECG from the patient’s body and the raw ECG is preprocessed and post processed. It is sent as input to the heart rate (HR) retrieval system. Then, the HR is sent as input to the PIC microcontroller. If any abnormalities are detected then accordingly, the alert signal is sent through the short messaging service (SMS), to indicate the patient¨s critical condition to the medical practitioner) and also as a self-alert to the patient.</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2</span><span class="elsevierStyleSectionTitle" id="sect0020">Existing systems</span><p id="par0015" class="elsevierStylePara elsevierViewall">To create a wireless sensor network system, that can continuously monitor and detect the cardiovascular disease and send the ECG wave of the patient to their mobile and to the central node which transmits the <a name="p134"></a>wave to the doctor too<a class="elsevierStyleCrossRef" href="#bib0005">[1]</a>. Using the pervasive technology, it is possible to collect the user symptoms and the onset of heart attack by analyzing ECG recordings <a class="elsevierStyleCrossRef" href="#bib0025">[5]</a>. Three different models are proposed to detect the pathological degeneration. They are cell model, heart model and chest model <a class="elsevierStyleCrossRef" href="#bib0040">[8]</a>. These three models cannot be finished yet because they require a lot of unknown parameters. Monitoring the cardiovascular patient is not feasible, to prevent further risks. Hence, the early detection is needed. Aholter is a device used in this paper to detect the myocardial malfunctioning <a class="elsevierStyleCrossRef" href="#bib0050">[10]</a>. By attaching the GSM to the holter, we were able to diagnose myocardial ischaemia and we also were able to detect therapeutic and preventive procedures. This procedure was mainly based on the ST versus HR relations <a class="elsevierStyleCrossRef" href="#bib0070">[14]</a>. This paper proposes a model to analyze the electrocardiography using the advancement in the wireless technology.</p></span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">3</span><span class="elsevierStyleSectionTitle" id="sect0025">Problem formulation</span><p id="par0020" class="elsevierStylePara elsevierViewall">After the evaluation of the systems cited above <a class="elsevierStyleCrossRef" href="#bib0005">[1]</a>, <a class="elsevierStyleCrossRef" href="#bib0025">[5]</a>, <a class="elsevierStyleCrossRef" href="#bib0040">[8]</a>, <a class="elsevierStyleCrossRef" href="#bib0050">[10]</a>, <a class="elsevierStyleCrossRef" href="#bib0070">[14]</a>, the following troubles can be recognized. <ul class="elsevierStyleList" id="lis0005"><li class="elsevierStyleListItem" id="lsti0005"><span class="elsevierStyleLabel">a)</span><p id="par0025" class="elsevierStylePara elsevierViewall">In the traditional medical method, the mobility of both the doctor and the patient are restricted.</p></li><li class="elsevierStyleListItem" id="lsti0010"><span class="elsevierStyleLabel">b)</span><p id="par0030" class="elsevierStylePara elsevierViewall">All the existing methods are web based, without Internet they cannot be utilized.</p></li><li class="elsevierStyleListItem" id="lsti0015"><span class="elsevierStyleLabel">c)</span><p id="par0035" class="elsevierStylePara elsevierViewall">Some of the systems give false alarms due to various environmental constraints.</p></li><li class="elsevierStyleListItem" id="lsti0020"><span class="elsevierStyleLabel">d)</span><p id="par0040" class="elsevierStylePara elsevierViewall">Many existing systems use expensive hardware components and complex software tools.</p></li></ul></p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">4</span><span class="elsevierStyleSectionTitle" id="sect0030">Proposed work</span><p id="par0045" class="elsevierStylePara elsevierViewall">The ECG signal from the patient’s body is picked up using ECG/Infra-Red (IR) sensors. The obtained signal is protruded with internal and external noises. The voltage level of the myocardial waves are in millivolts. At this range the preprocessing and postprocessing is not possible at all, so the obtained signal must be amplified and filtered. For the filtering purpose the adaptive filter is used. In order to provide amplification and isolation to the patients, the bio-amplifier is used. At that point, the obtained analog signal is converted into digital using pic microcontroller where the A/D circuit is inbuilt. Then the comparison of QRS complex amplitude levels are done and the corresponding alarm signal will be activated as it is shown in <a class="elsevierStyleCrossRef" href="#f0005">Figure 1</a>.</p><elsevierMultimedia ident="f0005"></elsevierMultimedia><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">4.1</span><span class="elsevierStyleSectionTitle" id="sect0035">LM234 amplifier</span><p id="par0050" class="elsevierStylePara elsevierViewall">The LM234 is a three terminal device and its operating current level varies from 1µA to 10mA. The operating voltage level goes from 0.8V to 30V and the regulation is 0.02%/V. LM234 draws no reverse current and it can be used as a linear temperature sensor. The applications of LM234 are current limiter, micro-power bias network, buffer for photoconductive cell, current mode temperature sensing and constant-gain bias for bipolar stage.<a name="p135"></a></p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">4.2</span><span class="elsevierStyleSectionTitle" id="sect0040">Adaptive filter</span><p id="par0055" class="elsevierStylePara elsevierViewall">Even though the ECG is picked by using high technical and efficient sensors, it is impossible to have a noise free ECG signal. But if the noise level is too high then it leads to the wrong estimation about the state of the heart and patient’s health. Due to this high power, time consumption is required for the signal processing.</p><p id="par0060" class="elsevierStylePara elsevierViewall">Thus the filter section is very important. Some noises are P and T wave type, powerline interference, EMG from muscle, operating room condition. Mostly the filter option is bandpass filter. But it is not suitable if it is used for the elderly groups, hence, we choose the adaptive filter which adapts its nature by itself according to the time and range. It does not require any prior knowledge about the signal and the nature of the noise.</p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">4.3</span><span class="elsevierStyleSectionTitle" id="sect0045">Bio amplifier</span><p id="par0065" class="elsevierStylePara elsevierViewall">It is a combination of the instrumentation amplifier and a power amplifier. The instrumentation amplifier by itself is not enough to get the amplified QRS complex. So we utilize a power amplifier. It is an amplifier used to provide isolations. It amplifies the QRS complex by its own.</p></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">4.4</span><span class="elsevierStyleSectionTitle" id="sect0050">PIC microcontroller</span><p id="par0070" class="elsevierStylePara elsevierViewall">The PIC microcontroller is considered the heart of this proposed work and it has an inbuilt analog to digital converter (ADC). The PIC has a set of registers that can function as a general purpose RAM. Special control registers for hardware resources are mapped to data space. All PIC can handle the data in 8-bit chunks.</p><p id="par0075" class="elsevierStylePara elsevierViewall">The addressability unit of the code space is different to the data space. Actually, the code space can be implemented as ROM, EPROM and flash ROM. Generally, external code memory is indirectly addressable because of lacking external memory interface. The instructions can vary from low-end PIC to high-end PIC, with the low-end and high end PIC having instructions varying from about 35 instructions to over 80 instructions respectively. The features of the PIC micro controller are code efficiency, safety, instruction set, speed, static operation, drive capability. The advantages of PIC microcontroller include a brief instruction set to learn, a built in oscillator with selectable speed, inexpensive microcontrollers, and a wide range of interfaces including USB and Ethernet.</p></span></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">5</span><span class="elsevierStyleSectionTitle" id="sect0055">Algorithms for telemetry applications</span><p id="par0080" class="elsevierStylePara elsevierViewall">The following list shows the various detection algorithms that are available for telemetry/telemedicine applications. Some of them are listed below. <ul class="elsevierStyleList" id="lis0010"><li class="elsevierStyleListItem" id="lsti0025"><span class="elsevierStyleLabel"><span class="elsevierStyleInlineFigure"><elsevierMultimedia class="elsevierStyleLink" ident="fx1"></elsevierMultimedia></span></span><p id="par0085" class="elsevierStylePara elsevierViewall">Turning point algorithm</p></li><li class="elsevierStyleListItem" id="lsti0030"><span class="elsevierStyleLabel"><span class="elsevierStyleInlineFigure"><elsevierMultimedia class="elsevierStyleLink" ident="fx1"></elsevierMultimedia></span></span><p id="par0090" class="elsevierStylePara elsevierViewall">Aztec algorithm</p></li><li class="elsevierStyleListItem" id="lsti0035"><span class="elsevierStyleLabel"><span class="elsevierStyleInlineFigure"><elsevierMultimedia class="elsevierStyleLink" ident="fx1"></elsevierMultimedia></span></span><p id="par0095" class="elsevierStylePara elsevierViewall">Fan algorithm</p></li><li class="elsevierStyleListItem" id="lsti0040"><span class="elsevierStyleLabel"><span class="elsevierStyleInlineFigure"><elsevierMultimedia class="elsevierStyleLink" ident="fx1"></elsevierMultimedia></span></span><p id="par0100" class="elsevierStylePara elsevierViewall">QRS detection algorithm</p></li></ul></p><p id="par0105" class="elsevierStylePara elsevierViewall">Turning algorithm, even though it is simple and fast, it is not able to detect the wide QRS signal and it also produces time distortion. In Aztec algorithm, it is able to detect wide QRS but it does not produce fixed reduction rate. The QRS detection algorithm overcomes both disadvantages of turning and aztec algorithm. QRS detection algorithm is able to detect wide QRS and also produces constant reduction rate which is needed for the clinical process and it is shown in <a class="elsevierStyleCrossRef" href="#f0010">Figure. 2</a>.<a name="p136"></a></p><elsevierMultimedia ident="f0010"></elsevierMultimedia><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">5.1</span><span class="elsevierStyleSectionTitle" id="sect0060">QRS detection algorithm</span><p id="par0110" class="elsevierStylePara elsevierViewall">For this detection algorithm we design a bandpass filter from a special class of digital filter which only requires a coefficient. It is very difficult to design digital bandpass filter directly, therefore, we design a cascaded connection of lowpass and high pass filter. It attenuates the low frequency characteristics of P and T waves and baseline drifts and high frequency characteristics like power line interference and electrocardiographic noises.</p><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">5.1.1</span><span class="elsevierStyleSectionTitle" id="sect0065">Low pass filter</span><p id="par0115" class="elsevierStylePara elsevierViewall">The transfer function of the second order filter is</p><p id="par0120" class="elsevierStylePara elsevierViewall"><elsevierMultimedia ident="eq0005"></elsevierMultimedia></p><p id="par0125" class="elsevierStylePara elsevierViewall">The difference equation of the filter is</p><p id="par0130" class="elsevierStylePara elsevierViewall"><elsevierMultimedia ident="eq0010"></elsevierMultimedia></p><p id="par0135" class="elsevierStylePara elsevierViewall">This filter has purely linear phase response. Power line is highly attenuated. All the frequencies are attenuated by more than 250dB. Any 60Hz noise or muscle noises are highly attenuated.</p></span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">5.1.2</span><span class="elsevierStyleSectionTitle" id="sect0070">High pass filter</span><p id="par0140" class="elsevierStylePara elsevierViewall">The high pass filter is implemented by subtracting the low pass filter from all pass filters with delay. The low pass filter is an integer coefficient filter with transfer function,</p><p id="par0145" class="elsevierStylePara elsevierViewall"><elsevierMultimedia ident="eq0015"></elsevierMultimedia></p><p id="par0150" class="elsevierStylePara elsevierViewall">And the difference equation is:</p><p id="par0155" class="elsevierStylePara elsevierViewall"><elsevierMultimedia ident="eq0020"></elsevierMultimedia></p><p id="par0160" class="elsevierStylePara elsevierViewall">The transfer function of the high pass filter is:</p><p id="par0165" class="elsevierStylePara elsevierViewall"><elsevierMultimedia ident="eq0025"></elsevierMultimedia></p><p id="par0170" class="elsevierStylePara elsevierViewall">And the difference equation is:</p><p id="par0175" class="elsevierStylePara elsevierViewall"><elsevierMultimedia ident="eq0030"></elsevierMultimedia></p></span><span id="sec0065" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">5.1.3</span><span class="elsevierStyleSectionTitle" id="sect0075">Derivatives</span><p id="par0180" class="elsevierStylePara elsevierViewall">After the filtering process, the signal is differentiated to get a clear information about the slope of the QRS complex. A five point derivative has a transfer function of:</p><p id="par0185" class="elsevierStylePara elsevierViewall"><elsevierMultimedia ident="eq0035"></elsevierMultimedia></p><p id="par0190" class="elsevierStylePara elsevierViewall">the difference equation is given by:</p><p id="par0195" class="elsevierStylePara elsevierViewall"><elsevierMultimedia ident="eq0040"></elsevierMultimedia></p></span><span id="sec0070" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">5.1.4</span><span class="elsevierStyleSectionTitle" id="sect0080">Squaring function</span><p id="par0200" class="elsevierStylePara elsevierViewall">The squaring function is a non linear function. The equation that implements this function,</p><p id="par0205" class="elsevierStylePara elsevierViewall"><elsevierMultimedia ident="eq0045"></elsevierMultimedia></p><p id="par0210" class="elsevierStylePara elsevierViewall">This operation makes all the data point in the processed signal to be positive and also amplifies the output of the derivative non-linearly. In this operation the output of this stage is hard-limited to a certain level which is based on the number of bits used to represent the data type of the signal.</p></span><span id="sec0075" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">5.1.5</span><span class="elsevierStyleSectionTitle" id="sect0085">Moving window integral</span><p id="par0215" class="elsevierStylePara elsevierViewall">The slope of the R wave by itself, is not a guaranteed way to detect the QRS events. Many abnormal QRS complex has large amplitude and long duration. This type of complex cannot be detected using the information about slope of the R wave alone. To extract more information about the QRS complex this type of moving integral is used. It is implemented with the difference equation</p><p id="par0220" class="elsevierStylePara elsevierViewall"><elsevierMultimedia ident="eq0050"></elsevierMultimedia></p><p id="par0225" class="elsevierStylePara elsevierViewall">Where, N - Number of samples in the width of the moving integral</p></span></span><span id="sec0080" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">5.2</span><span class="elsevierStyleSectionTitle" id="sect0090">Why ECG QRS detection algorithm?</span><p id="par0230" class="elsevierStylePara elsevierViewall">The size of the moving window is chosen by experiment; <ul class="elsevierStyleList" id="lis0015"><li class="elsevierStyleListItem" id="lsti0045"><span class="elsevierStyleLabel"><span class="elsevierStyleInlineFigure"><elsevierMultimedia class="elsevierStyleLink" ident="fx1"></elsevierMultimedia></span></span><p id="par0235" class="elsevierStylePara elsevierViewall">If the size is too big then the QRS complex merges with the T wave.<a name="p137"></a></p></li><li class="elsevierStyleListItem" id="lsti0050"><span class="elsevierStyleLabel"><span class="elsevierStyleInlineFigure"><elsevierMultimedia class="elsevierStyleLink" ident="fx1"></elsevierMultimedia></span></span><p id="par0240" class="elsevierStylePara elsevierViewall">If the size is too small then the QRS complex produces several peaks at the output.</p></li></ul></p><p id="par0245" class="elsevierStylePara elsevierViewall">The first two algorithms are only useful for data reduction. With this we are only able to reduce the memory space needed to store the ECG wave. We need an effective algorithm to detect the QRS complex from the ECG signal. Since we are only analysyng the QRS complex which causes high risk to the patients.</p></span></span><span id="sec0085" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">6</span><span class="elsevierStyleSectionTitle" id="sect0095">Results and discussion</span><span id="sec0090" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">6.1</span><span class="elsevierStyleSectionTitle" id="sect0100">Simulation results–analysis of ECG noise removal & abnormality detection</span><p id="par0250" class="elsevierStylePara elsevierViewall">LabVIEW is a software tool used to simulate the system and get the response to know how well the system is working according to our expectation. Since LabVIEW is highly reliable and user friendly both, the electrical and electronic device systems can be simulated. The first stage in the QRS detection is noise reduction and recovering the original signal from the noise contained signal as shown in <a class="elsevierStyleCrossRef" href="#f0015">Figures. 3</a> and <a class="elsevierStyleCrossRef" href="#f0020">4</a>.</p><elsevierMultimedia ident="f0015"></elsevierMultimedia><elsevierMultimedia ident="f0020"></elsevierMultimedia><p id="par0255" class="elsevierStylePara elsevierViewall">There are various types of noise such as base line fluctuation, 60Hz artifact, 60Hz artifact amplitude, DC offset, randomize magnitude. For the noise removal unit, we have chosen the adaptive filter, the filtering process which will be explained below. One sample ECG signal is taken from the ECG simulator, then we add a type of noise as mentioned above. Then we take the adaptive (wavelet) transformation for that signal as shown in <a class="elsevierStyleCrossRef" href="#f0020">Figure 4</a>. Then the signal looks like a fully contaminated signal.</p><p id="par0260" class="elsevierStylePara elsevierViewall">Inverse adaptive transformation is taken to reconstruct the original signal as such. From it, the feasibility of recovering the original signal from the noise by using adaptive filter can be proved. Afterward, the same procedure is followed to reduce the other type of noises.</p><p id="par0265" class="elsevierStylePara elsevierViewall">But here we add and test the particular 60 Hz noise only because it is the major noise in the real time ECG signal. The same adaptive filtration is performed to recover the signal.</p><p id="par0270" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#f0025">Figure.5</a> shows the actual implementation of the adaptive filter. On it we use all the standard signals and all kind of noises as shown in <a class="elsevierStyleCrossRef" href="#f0030">Figures 6</a> and <a class="elsevierStyleCrossRef" href="#f0035">7</a>.</p><elsevierMultimedia ident="f0025"></elsevierMultimedia><elsevierMultimedia ident="f0030"></elsevierMultimedia><elsevierMultimedia ident="f0035"></elsevierMultimedia><p id="par0275" class="elsevierStylePara elsevierViewall">We take one standard signal and add different type of noises one by one and try to recover the signal using the same process. The standard signal and noise is added and it is passed through the adaptive filter. After processing it, the original signal is recovered as in <a class="elsevierStyleCrossRef" href="#f0035">Figure 7</a>.</p><p id="par0280" class="elsevierStylePara elsevierViewall">From the previous discussion, QRS detection system and the tele - alerting mechanism can be implemented without the dominant disturbances from the various types of noises by using the LabVIEW.<a name="p138"></a><a name="p139"></a><a name="p140"></a></p><p id="par0285" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#f0040">Figure: 8</a> we use ECG simulator to create a standard ECG signal. The signal is then filtered and amplified and processed to detect the QRS complex. Here the peaks and valleys of the ECG signal is found and analyzed. The duration of the process can be selected manually.</p><elsevierMultimedia ident="f0040"></elsevierMultimedia><p id="par0290" class="elsevierStylePara elsevierViewall">If the peak is found then the location of the peak and the amplitude level of the peak is calculated. If more than one peak is found then the average for the peaks can be calculated and the maximum peak value is calculated and all this are calculated for the valleys also. Then the maximum peak to peak value and minimum peak to peak value is generated as shown in <a class="elsevierStyleCrossRef" href="#f0045">Figure 9</a>.</p><elsevierMultimedia ident="f0045"></elsevierMultimedia><p id="par0295" class="elsevierStylePara elsevierViewall">LabVIEW is the best tool for analyzing the ECG for the single user; it is analyzed based on the following procedure. The RED and GREEN colour points in the above windows (<a class="elsevierStyleCrossRef" href="#f0045">Figure 9</a>) denotes the peaks and valleys in the ECG signal. The RED denotes the peaks and the GREEN denotes the valleys. The QRS detection is done either between two GREEN points (time interval) or between te two GREEN and one RED point (amplitude).</p><p id="par0300" class="elsevierStylePara elsevierViewall">If the ECG and the heart rate (HR) are normal (70<span class="elsevierStyleHsp" style=""></span>>HR && HR<span class="elsevierStyleHsp" style=""></span><90), then the GREEN light will glow, if the ECG and the heart rate (HR) are abnormal(HR>90 && HR<70) then automatically the RED light will glow as shown in <a class="elsevierStyleCrossRef" href="#f0050">Figure 10</a>. On it, the amplitude of the ECG signal is varied manually by using the ECG simulator and then response is checked. The GREEN light glows for the normal ECG amplitude (≈ 1.6mVolts) and the RED light glows for the abnormal ECG amplitude (≠ 1.6mVolts). This shows that this optimal method is well suited for the early detection of myocardial infarctions immediately.</p><elsevierMultimedia ident="f0050"></elsevierMultimedia></span><span id="sec0095" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">6.2</span><span class="elsevierStyleSectionTitle" id="sect0105">Numerical analysis</span><p id="par0305" class="elsevierStylePara elsevierViewall">If the analysis of the ECG signal is to be done for ‘N’ number of users then the LabVIEW is not an optimal way. For this we can use LabChart, the procedure is more or less the same one as that of the LabVIEW.</p><p id="par0310" class="elsevierStylePara elsevierViewall">For ‘N’ number of users the detection is not based on the single amplitude value. The mean and standard deviation are calculated for the ‘N’ users to carry out the numerical analysis as shown in <a class="elsevierStyleCrossRef" href="#f0055">Figure 11</a>. <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>, shows the mean and standard deviation for abnormal cardiac patients, N<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>25 and 209.<a name="p141"></a></p><elsevierMultimedia ident="f0055"></elsevierMultimedia><elsevierMultimedia ident="tbl0005"></elsevierMultimedia></span></span><span id="sec0100" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">7</span><span class="elsevierStyleSectionTitle" id="sect0110">Conclusion</span><p id="par0315" class="elsevierStylePara elsevierViewall">The proposed system could be used as a forewarning system for monitoring during normal activity or physical exercises. The most important value of this developed cardiac tele-monitoring system lies in the detection of ECG/Heart Rate of the patients who are located in remote areas or are traveling thus, are not in position of reporting to the doctor for immediate treatment. An alert SMS can be transmitted using the GSM technology to the doctors and help can be look for in order to save the patient´s life. The proposed system enables the heart patient to be on the move globally and keep on with his day to day work. In clinical application, we find that it can detect the difference between cardiovascular patients and normal persons. Comparing this developed cardiac tele-monitoring system to the existing systems <a class="elsevierStyleCrossRefs" href="#bib0005">[1-19]</a>, it has the following enhanced features. <ul class="elsevierStyleList" id="lis0020"><li class="elsevierStyleListItem" id="lsti0055"><span class="elsevierStyleLabel"><span class="elsevierStyleInlineFigure"><elsevierMultimedia class="elsevierStyleLink" ident="fx1"></elsevierMultimedia></span></span><p id="par0320" class="elsevierStylePara elsevierViewall">Do not requires any Internet facility.</p></li><li class="elsevierStyleListItem" id="lsti0060"><span class="elsevierStyleLabel"><span class="elsevierStyleInlineFigure"><elsevierMultimedia class="elsevierStyleLink" ident="fx1"></elsevierMultimedia></span></span><p id="par0325" class="elsevierStylePara elsevierViewall">No need for any centralised server.</p></li><li class="elsevierStyleListItem" id="lsti0065"><span class="elsevierStyleLabel"><span class="elsevierStyleInlineFigure"><elsevierMultimedia class="elsevierStyleLink" ident="fx1"></elsevierMultimedia></span></span><p id="par0330" class="elsevierStylePara elsevierViewall">Low fault tolerance level is<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>4 %</p></li><li class="elsevierStyleListItem" id="lsti0070"><span class="elsevierStyleLabel"><span class="elsevierStyleInlineFigure"><elsevierMultimedia class="elsevierStyleLink" ident="fx1"></elsevierMultimedia></span></span><p id="par0335" class="elsevierStylePara elsevierViewall">Portable and easy to handle.</p></li><li class="elsevierStyleListItem" id="lsti0075"><span class="elsevierStyleLabel"><span class="elsevierStyleInlineFigure"><elsevierMultimedia class="elsevierStyleLink" ident="fx1"></elsevierMultimedia></span></span><p id="par0340" class="elsevierStylePara elsevierViewall">Very low cost.</p></li><li class="elsevierStyleListItem" id="lsti0080"><span class="elsevierStyleLabel"><span class="elsevierStyleInlineFigure"><elsevierMultimedia class="elsevierStyleLink" ident="fx1"></elsevierMultimedia></span></span><p id="par0345" class="elsevierStylePara elsevierViewall">Low power consumption.</p></li></ul></p><p id="par0350" class="elsevierStylePara elsevierViewall">These study results have great significance in researching, preventing and finding epidemics in the cardiovascular system throughout the country.<a name="p142"></a><a name="p143"></a></p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:11 [ 0 => array:3 [ "identificador" => "xres498730" "titulo" => "Abstract" "secciones" => array:1 [ 0 => array:1 [ "identificador" => "abst0005" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec520264" "titulo" => "Keywords" ] 2 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 3 => array:2 [ "identificador" => "sec0010" "titulo" => "Existing systems" ] 4 => array:2 [ "identificador" => "sec0015" "titulo" => "Problem formulation" ] 5 => array:3 [ "identificador" => "sec0020" "titulo" => "Proposed work" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "sec0025" "titulo" => "LM234 amplifier" ] 1 => array:2 [ "identificador" => "sec0030" "titulo" => "Adaptive filter" ] 2 => array:2 [ "identificador" => "sec0035" "titulo" => "Bio amplifier" ] 3 => array:2 [ "identificador" => "sec0040" "titulo" => "PIC microcontroller" ] ] ] 6 => array:3 [ "identificador" => "sec0045" "titulo" => "Algorithms for telemetry applications" "secciones" => array:2 [ 0 => array:3 [ "identificador" => "sec0050" "titulo" => "QRS detection algorithm" "secciones" => array:5 [ 0 => array:2 [ "identificador" => "sec0055" "titulo" => "Low pass filter" ] 1 => array:2 [ "identificador" => "sec0060" "titulo" => "High pass filter" ] 2 => array:2 [ "identificador" => "sec0065" "titulo" => "Derivatives" ] 3 => array:2 [ "identificador" => "sec0070" "titulo" => "Squaring function" ] 4 => array:2 [ "identificador" => "sec0075" "titulo" => "Moving window integral" ] ] ] 1 => array:2 [ "identificador" => "sec0080" "titulo" => "Why ECG QRS detection algorithm?" ] ] ] 7 => array:3 [ "identificador" => "sec0085" "titulo" => "Results and discussion" "secciones" => array:2 [ 0 => array:2 [ "identificador" => "sec0090" "titulo" => "Simulation results–analysis of ECG noise removal & abnormality detection" ] 1 => array:2 [ "identificador" => "sec0095" "titulo" => "Numerical analysis" ] ] ] 8 => array:2 [ "identificador" => "sec0100" "titulo" => "Conclusion" ] 9 => array:2 [ "identificador" => "xack161119" "titulo" => "Acknowledgement" ] 10 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "PalabrasClave" => array:1 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec520264" "palabras" => array:5 [ 0 => "electrocardiography (ECG)" 1 => "global system for mobile (GSM)" 2 => "telemetry" 3 => "LabVIEW" 4 => "wireless transmission" ] ] ] ] "tieneResumen" => true "resumen" => array:1 [ "en" => array:2 [ "titulo" => "Abstract" "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Myocardial malfunctioning harms human health and since most people are affected by arrhythmia. It is one of the major causes for sudden death. The chance of occurrence of severe myocardial problem will be increased after the first heart block. Detecting the onset of malfunctioning is ever challenging. This paper proposes to find out how the onset of myocardial problem can be detected automatically in an early stage by an uninterrupted surveillance device. When the device detects any abnormal myocardial functions, then it automatically alerts the patient and the doctor through the GSM (global system for mobile) mobile phones and the patient will be given first order medical attention as soon as possible. Eventually this increases greatly the chance of survival of the victim.</p></span>" ] ] "multimedia" => array:22 [ 0 => array:7 [ "identificador" => "f0005" "etiqueta" => "Figure 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 754 "Ancho" => 1825 "Tamanyo" => 117468 ] ] "descripcion" => array:1 [ "en" => "<p id="sp0005" class="elsevierStyleSimplePara elsevierViewall">Block diagram of proposed cardiac Tele-monitoring system.</p>" ] ] 1 => array:7 [ "identificador" => "f0010" "etiqueta" => "Figure 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 527 "Ancho" => 1846 "Tamanyo" => 50295 ] ] "descripcion" => array:1 [ "en" => "<p id="sp0010" class="elsevierStyleSimplePara elsevierViewall">Steps involved in ECG QRS detection algorithm for tele-monitoring system.</p>" ] ] 2 => array:7 [ "identificador" => "f0015" "etiqueta" => "Figure 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 788 "Ancho" => 1716 "Tamanyo" => 143058 ] ] "descripcion" => array:1 [ "en" => "<p id="sp0015" class="elsevierStyleSimplePara elsevierViewall">LabVIEW design for of ECG noise analysis.</p>" ] ] 3 => array:7 [ "identificador" => "f0020" "etiqueta" => "Figure 4" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr4.jpeg" "Alto" => 852 "Ancho" => 1735 "Tamanyo" => 301039 ] ] "descripcion" => array:1 [ "en" => "<p id="sp0020" class="elsevierStyleSimplePara elsevierViewall">Feasibility result for the noise removal of ECG.</p>" ] ] 4 => array:7 [ "identificador" => "f0025" "etiqueta" => "Figure 5" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr5.jpeg" "Alto" => 899 "Ancho" => 1770 "Tamanyo" => 159891 ] ] "descripcion" => array:1 [ "en" => "<p id="sp0025" class="elsevierStyleSimplePara elsevierViewall">LabVIEW design for adaptive filter to produce noise-limited ECG.</p>" ] ] 5 => array:7 [ "identificador" => "f0030" "etiqueta" => "Figure 6" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr6.jpeg" "Alto" => 964 "Ancho" => 1906 "Tamanyo" => 324584 ] ] "descripcion" => array:1 [ "en" => "<p id="sp0030" class="elsevierStyleSimplePara elsevierViewall">Noise-limited ECG from adaptive filter and heart rate estimation.</p>" ] ] 6 => array:7 [ "identificador" => "f0035" "etiqueta" => "Figure 7" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr7.jpeg" "Alto" => 979 "Ancho" => 1886 "Tamanyo" => 419059 ] ] "descripcion" => array:1 [ "en" => "<p id="sp0035" class="elsevierStyleSimplePara elsevierViewall">Contaminated (Noisy) ECG and filter-out (Noise-Limited) ECG.</p>" ] ] 7 => array:7 [ "identificador" => "f0040" "etiqueta" => "Figure 8" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr8.jpeg" "Alto" => 952 "Ancho" => 1897 "Tamanyo" => 272208 ] ] "descripcion" => array:1 [ "en" => "<p id="sp0040" class="elsevierStyleSimplePara elsevierViewall">LabVIEW Design for ECG detection (peak and valley detection).</p>" ] ] 8 => array:7 [ "identificador" => "f0045" "etiqueta" => "Figure 9" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr9.jpeg" "Alto" => 1002 "Ancho" => 1721 "Tamanyo" => 216502 ] ] "descripcion" => array:1 [ "en" => "<p id="sp0045" class="elsevierStyleSimplePara elsevierViewall">Peak and Valley Detection of ECG wave with its Max. & Min Values.</p>" ] ] 9 => array:7 [ "identificador" => "f0050" "etiqueta" => "Figure 10" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr10.jpeg" "Alto" => 605 "Ancho" => 872 "Tamanyo" => 124167 ] ] "descripcion" => array:1 [ "en" => "<p id="sp0050" class="elsevierStyleSimplePara elsevierViewall">Abnormality detection of ECG wave in terms of amplitude level.</p>" ] ] 10 => array:7 [ "identificador" => "f0055" "etiqueta" => "Figure 11" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr11.jpeg" "Alto" => 1314 "Ancho" => 1904 "Tamanyo" => 568087 ] ] "descripcion" => array:1 [ "en" => "<p id="sp0055" class="elsevierStyleSimplePara elsevierViewall">Labchart analysis of ECG mean, standard deviation for ‘N’ patients.</p>" ] ] 11 => array:7 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:1 [ "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head " rowspan="2" align="center" valign="top" scope="col">Variables</th><th class="td" title="table-head " colspan="2" align="center" valign="top" scope="col">Abnormal ECG</th><th class="td" title="table-head " rowspan="2" align="center" valign="top" scope="col">P-value</th></tr><tr title="table-row"><th class="td" title="table-head " align="center" valign="top" scope="col" style="border-bottom: 2px solid black">Yes Mean±<span class="elsevierStyleHsp" style=""></span>SD N<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>25 \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="center" valign="top" scope="col" style="border-bottom: 2px solid black">No Mean±<span class="elsevierStyleHsp" style=""></span>SD N<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>209 \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="table-entry " align="center" valign="top">Heart rate/min \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="center" valign="top">101.1±17.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="center" valign="top">101.2<span class="elsevierStyleHsp" style=""></span>±11.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="center" valign="top">0.9 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="center" valign="top">Systolic blood pressure standing(mmHg) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="center" valign="top">102.8±15.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="center" valign="top">104.1±10.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="center" valign="top">0.09 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="center" valign="top">Systolic blood pressure supine(mmHg) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="center" valign="top">103.9±14.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="center" valign="top">108.5±16.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="center" valign="top">0.7 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="center" valign="top">Diastolic blood pressure standing (mmHg) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="center" valign="top">61.1±9.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="center" valign="top">53.0±9.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="center" valign="top">0.3 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="center" valign="top">Diastolic blood pressure supine(mmHg) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="center" valign="top">65.4±11.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="center" valign="top">68.9±5.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="center" valign="top">0.01 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="center" valign="top">Systolic difference (supine – standing) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="center" valign="top">3.5<span class="elsevierStyleHsp" style=""></span>±12.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="center" valign="top">8.6±9.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="center" valign="top">0.02 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="center" valign="top">Diastolic difference (supine – standing) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="center" valign="top">2.2±10.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="center" valign="top">14.6±8.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="center" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab796195.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Consolidated chart- mean & standard deviation for ‘N’ abnormal cardiac patients.</p>" ] ] 12 => array:6 [ "identificador" => "eq0005" "etiqueta" => "(1)" "tipo" => "MULTIMEDIAFORMULA" "mostrarFloat" => false "mostrarDisplay" => true "Formula" => array:5 [ "Matematica" => "H(z)=(1-z-6)/(1-z-1)2" "Fichero" => "si1.jpeg" "Tamanyo" => 1576 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This fabulous idea and improvement of this proposed work was done as per the valuable guidelines of Dr.(Mrs).R.Sukanesh, B.E., M.E., Ph.D., P.G.D.H.E., Senior Professor, Department of ECE, Thiagarajar College of Engineering, Madurai.</p>" "vista" => "all" ] ] ] "idiomaDefecto" => "en" "url" => "/16656423/0000001100000001/v2_201505081621/S1665642313715220/v2_201505081621/en/main.assets" "Apartado" => null "PDF" => "https://static.elsevier.es/multimedia/16656423/0000001100000001/v2_201505081621/S1665642313715220/v2_201505081621/en/main.pdf?idApp=UINPBA00004N&text.app=https://www.elsevier.es/" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S1665642313715220?idApp=UINPBA00004N" ]
| Year/Month | Html | Total | |
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| 2020 October | 33 | 5 | 38 |
| 2020 September | 29 | 6 | 35 |
| 2020 August | 31 | 10 | 41 |
| 2020 July | 22 | 1 | 23 |
| 2020 June | 17 | 4 | 21 |
| 2020 May | 37 | 4 | 41 |
| 2020 April | 29 | 4 | 33 |
| 2020 March | 41 | 6 | 47 |
| 2020 February | 31 | 2 | 33 |
| 2020 January | 22 | 6 | 28 |
| 2019 December | 27 | 4 | 31 |
| 2019 November | 19 | 6 | 25 |
| 2019 October | 28 | 7 | 35 |
| 2019 September | 19 | 6 | 25 |
| 2019 August | 22 | 3 | 25 |
| 2019 July | 33 | 12 | 45 |
| 2019 June | 67 | 12 | 79 |
| 2019 May | 140 | 15 | 155 |
| 2019 April | 73 | 19 | 92 |
| 2019 March | 13 | 8 | 21 |
| 2019 February | 14 | 4 | 18 |
| 2019 January | 12 | 3 | 15 |
| 2018 December | 19 | 3 | 22 |
| 2018 November | 25 | 2 | 27 |
| 2018 October | 52 | 4 | 56 |
| 2018 September | 79 | 5 | 84 |
| 2018 August | 46 | 2 | 48 |
| 2018 July | 41 | 2 | 43 |
| 2018 June | 38 | 1 | 39 |
| 2018 May | 59 | 14 | 73 |
| 2018 April | 65 | 4 | 69 |
| 2018 March | 60 | 1 | 61 |
| 2018 February | 32 | 1 | 33 |
| 2018 January | 32 | 3 | 35 |
| 2017 December | 34 | 0 | 34 |
| 2017 November | 34 | 1 | 35 |
| 2017 October | 45 | 3 | 48 |
| 2017 September | 32 | 5 | 37 |
| 2017 August | 33 | 4 | 37 |
| 2017 July | 16 | 0 | 16 |
| 2017 June | 31 | 19 | 50 |
| 2017 May | 45 | 16 | 61 |
| 2017 April | 28 | 9 | 37 |
| 2017 March | 20 | 96 | 116 |
| 2017 February | 37 | 15 | 52 |
| 2017 January | 18 | 4 | 22 |
| 2016 December | 22 | 7 | 29 |
| 2016 November | 34 | 8 | 42 |
| 2016 October | 43 | 10 | 53 |
| 2016 September | 54 | 7 | 61 |
| 2016 August | 59 | 5 | 64 |
| 2016 July | 29 | 3 | 32 |
| 2016 June | 32 | 10 | 42 |
| 2016 May | 52 | 14 | 66 |
| 2016 April | 43 | 14 | 57 |
| 2016 March | 59 | 19 | 78 |
| 2016 February | 46 | 9 | 55 |
| 2016 January | 19 | 8 | 27 |
| 2015 December | 21 | 9 | 30 |
| 2015 November | 25 | 11 | 36 |
| 2015 October | 24 | 9 | 33 |
| 2015 September | 22 | 4 | 26 |
| 2015 August | 10 | 6 | 16 |
| 2015 July | 6 | 3 | 9 |
| 2015 May | 1 | 1 | 2 |
| 2015 April | 2 | 1 | 3 |
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