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Background Peripheral Pulse Analyser (PPA) is developed by Bhabha Atomic Research Centre (BARC) Mumbai. The recording of peripheral pulses is a highly important and necessary non-invasive technology used by clinicians to diagnose numerous disorders. Nonexperts may struggle to estimate waveforms accurately; motion artefacts may emerge during tonometry measurements if the skin-sensor contact pressure is insufficient. The aim of waveform analysis was to study differentiating parameters of PPA waveforms for early disease detection. Methods We used 70 subjects in two groups as healthy and unhealthy subjects. The pulse morphology is seen to be different in healthy and unhealthy subject. Each PPA signal repeats two or three pulse morphologies. Study was performed at Saraswati College of Engineering (SCOE), Kharghar in collaboration with Board of Research in Nuclear Sciences (BRNS). Findings: With peaks and valleys different parameters were introduced for eight pulse morphologies (P1 to P8). Polarity, amplitude and intervals are key parameters for waveform analysis of P1 to P8. Novelty: Automatically analysed blood flow variation of datasets can be used to classify the patterns into various classes to detect diseases. Each PPA signal repeats two or three pulse morphology for the duration of 300 seconds. These pulse morphologies differ in healthy and unhealthy subject. The percentage of pulse morphologies in each PPA signal decides diseases like hypertension, diabetes, and coronary artery diseases (CAD) etc. The waveforms are analysed and tested and are used for disease detection. we have obtained satisfactory results with 96% accuracy.
Background Peripheral Pulse Analyser (PPA) is developed by Bhabha Atomic Research Centre (BARC) Mumbai. The recording of peripheral pulses is a highly important and necessary non-invasive technology used by clinicians to diagnose numerous disorders. Nonexperts may struggle to estimate waveforms accurately; motion artefacts may emerge during tonometry measurements if the skin-sensor contact pressure is insufficient. The aim of waveform analysis was to study differentiating parameters of PPA waveforms for early disease detection. Methods We used 70 subjects in two groups as healthy and unhealthy subjects. The pulse morphology is seen to be different in healthy and unhealthy subject. Each PPA signal repeats two or three pulse morphologies. Study was performed at Saraswati College of Engineering (SCOE), Kharghar in collaboration with Board of Research in Nuclear Sciences (BRNS). Findings: With peaks and valleys different parameters were introduced for eight pulse morphologies (P1 to P8). Polarity, amplitude and intervals are key parameters for waveform analysis of P1 to P8. Novelty: Automatically analysed blood flow variation of datasets can be used to classify the patterns into various classes to detect diseases. Each PPA signal repeats two or three pulse morphology for the duration of 300 seconds. These pulse morphologies differ in healthy and unhealthy subject. The percentage of pulse morphologies in each PPA signal decides diseases like hypertension, diabetes, and coronary artery diseases (CAD) etc. The waveforms are analysed and tested and are used for disease detection. we have obtained satisfactory results with 96% accuracy.
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