Use of machine learning for unsupervised learning in the detection of abnormalities in the heart signal

Almarie Rivera-Hernandez; Jonathan Rodriguez-Figueroa; Santiago Goenaga-Buelvas; Samira Ortiz-Rodriguez; Eduardo Castillo-Charris; Miguel Goenaga-Jimenez

doi:10.1117/12.2664158

14 June 2023 Use of machine learning for unsupervised learning in the detection of abnormalities in the heart signal

Almarie Rivera-Hernandez, Jonathan Rodriguez-Figueroa, Santiago Goenaga-Buelvas, Samira Ortiz-Rodriguez, Eduardo Castillo-Charris, Miguel Goenaga-Jimenez

Author Affiliations +

Proceedings Volume 12547, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII; 125470X (2023) https://doi.org/10.1117/12.2664158
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

The heart is an important organ in the human body. The heart pumps blood throughout the body. This mechanical action is generated by electrical signals which can be measured. Measuring this electrical signal, we can perform a series of diagnostics to examine different functions of the heart. The electrocardiogram is a tool to record this activity with the purpose of examining the condition of the conductive system in terms of the timing of the activity of the cardiac muscle. The activity recorded by the ECG is the net electric activity between different points around the body. Using the ECG and the radial pulse we can find the cardiac rhythm in a given situation. Variations in the parameters of these signals could mean possible malfunctions of the conduction system. Most of these variations are known and have been related to heart diseases. Arrhythmias can also be determined using the ECG. In this paper, we will record the ECG of each person in the group, and we will determine a variety of parameters of the cardiac system, including the cardiac vector, the cardiac rate, and the P-R interval. Using the ECG recordings, we will train a neural network, in an unsupervised way, to learn the different types of signals for different individuals. We will also investigate the possible sources of distortion of the signal as well as the effect of inspiration and expiration on the recording of the ECG. In preliminary data obtained, the ECG signals have frequency averages between 0.38, 0.39 and 0.4 Hertz, for individuals between 20 and 25 years old.

Conference Presentation

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Almarie Rivera-Hernandez, Jonathan Rodriguez-Figueroa, Santiago Goenaga-Buelvas, Samira Ortiz-Rodriguez, Eduardo Castillo-Charris, and Miguel Goenaga-Jimenez "Use of machine learning for unsupervised learning in the detection of abnormalities in the heart signal", Proc. SPIE 12547, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII, 125470X (14 June 2023); https://doi.org/10.1117/12.2664158

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