Non linear and Dynamic Time Warping classification of morphological patterns identified from Plethysmographic observations in the radial pulse

Karamchandani, Sunil; Panju, Maysum; Mustafa, H. D.; Merchant, S. N.; Desai, U.B.; Gd, Jindal

doi:10.1109/icics.2011.6174292

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…14 In addition to heart rate variability studied worldwide, few researchers have studied blood pressure variability and peripheral blood flow variability. 79 Changes in the morphology of the peripheral pulse were noticed during these studies and different methods were tried to quantify the morphological changes 10,11 with a yield of 80 to 90%.…”

Section: Discussionmentioning

confidence: 99%

“…Karamchandani et al 10,11 have tried several methods for auto matic identification of these patterns including dynamic time warping, parallel support vector, etc. Classi fication of these waveforms using dynamic time warping yielded an accuracy of more than 94%, efficient predictive values, and statistics evaluating measures such as MCC and kappa MGMJMS met partial success.…”

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confidence: 99%

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Peripheral Pulse Morphology for Early Detection of Coronary Artery Disease

Warrier¹,

Deshpande²,

Athavale³

et al. 2014

MGM Journal of Medical Sciences

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Based on the past observation of recording abnormal impe dance cardiogram (ICG) in 13% normal subjects and subse quent detection of coronary artery disease (CAD) in majority of these cases, led the authors to record peripheral impedance plethysmograms (IPG) in control subjects and patients using impedance cardiovasograph, developed by electronics division, Bhabha Atomic Research Centre (BARC). Analysis of peripheral plethysmograms, thus recorded, has shown 8 dominant morphological patterns of the peripheral pulses depending upon their status of health. In cognizance of these observations, different methods of pattern analysis were used for pattern identification. Fourier Transform based method has been observed to yield higher diagnostic yield. Morphology index (MI) of the peripheral pulse derived from this method was observed to vary from 0.28 to 1, the former indicating the poorest and later the normal health. Among 100 subjects suffering from various disorders, 8 patients with coronary artery disease have recorded average index to be between 0.30 and 0.45.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Peripheral Pulse Morphology for Early Detection of Coronary Artery Disease

Warrier¹,

Deshpande²,

Athavale³

et al. 2014

MGM Journal of Medical Sciences

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Classification of Peripheral Pulse Morphology Using Convolutional Neural Networks

Patil,

Rai,

Nagare

2024

2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM)

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Role of machine learning techniques in classification of peripheral pulse morphology

Patil

Bhat²,

Christopher³

et al. 2023

MGMJ

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Recording of peripheral pulse serves as a very important and essential non-invasive tool used widely by doctors for the diagnosis of various diseases. The morphology of pulse is seen to vary as a function of time in a given individual and also from individual to individual. There are many variations in morphological patterns of peripheral pulse in different disease conditions, which lead to difficulty in accurate diagnosis. The peripheral pulse waveforms are extracted from radial arteries as time series data using a peripheral pulse analyzer which is designed on the principle of impedance plethysmography. It was first introduced by Nyober in the mid-nineteen hundreds and ameliorated further by Kubicek. It involves the recording of the instantaneous blood volume by the measurement of electrical impedance as a function of time. Therefore, the study of peripheral pulse morphology has gained much attention in the past few years among researchers. Physiological variability is one of the recent investigations added during the last two decades for the objective assessment of autonomic function and the assessment of prognosis in severe sicknesses namely myocardial infarction, diabetic neuropathy, etc. In addition to heart rate variability studied worldwide, few researchers have studied blood pressure variability and peripheral blood flow variability. In this computer era, artificial intelligence and machine learning techniques have become more important day-by-day, and different types of algorithms were used for the identification of hidden patterns from plethysmographic observations on the radial pulse such as support vector machine as well as crisp and fuzzy clustering. Eight patterns were classified with a yield of 80%–90% and helped with the diagnosis of disorders such as myocardial infarction, pulmonary tuberculosis, coronary artery disorders, cirrhosis of the liver, and bronchial asthma. This paper briefly describes the use of machine learning techniques for the classification of peripheral pulse morphologies.

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Non linear and Dynamic Time Warping classification of morphological patterns identified from Plethysmographic observations in the radial pulse

Cited by 3 publications

References 11 publications

Peripheral Pulse Morphology for Early Detection of Coronary Artery Disease

Peripheral Pulse Morphology for Early Detection of Coronary Artery Disease

Classification of Peripheral Pulse Morphology Using Convolutional Neural Networks

Role of machine learning techniques in classification of peripheral pulse morphology

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