Automated Screening of Myocardial Infarction Based on Statistical Analysis of Photoplethysmographic Data

• Published in: IEEE transactions on instrumentation and measurement Vol. 69; no. 6; pp. 2881 - 2890
• Main Authors: Sadhukhan, Deboleena, Dhar, Sandipan, Pal, Saurabh, Mitra, Madhuchhanda
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Automation; Blood; Classification; Computer-aided analysis; Data acquisition; Data integration; Electrocardiography; Feature extraction; Health care; Heart; Heart attacks; medical expert systems; Monitoring; Myocardial infarction; myocardial infarction (MI); Noise sensitivity; Parameter sensitivity; pattern analysis; photoplethysmography; Portable equipment; Process parameters; Signal monitoring; Signal processing; Statistical analysis; Telemedicine
• DOI: 10.1109/TIM.2019.2930438
• ISSN: 0018-9456

Integration of automated cardiac analysis techniques with the personal healthcare devices can aid in the timely diagnosis of myocardial infarction (MI) to reduce the risk of mortality. The use of electrocardiogram (ECG) along with advanced signal processing tools limits the application of the available techniques for such portable devices having limited processing ability. Here we propose a novel MI screening technique based on photoplethysmographic (PPG) data, which is the most widely monitored signal in everyday health monitoring. Two simple statistical features ideally reflecting the MI-induced variability in the PPG data are identified. To combat the noise sensitivity of the PPG acquisition process, the same statistical parameters are employed for eliminating the noise corrupted data segments. Classification of MI data is then performed using a simple threshold-based binary classification approach. The technique is evaluated with PPG records collected from 62 hospitalized MI patients and 65 healthy subjects. The robustness of the algorithm is cross-validated with both MI and non-MI records collected from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) II database. The algorithm achieves an average detection accuracy of 96.25% with sensitivity and specificity of 96.36% and 96.15%, respectively. The promising results establish the utility of PPG as an alternate MI diagnosing tool. Apart from using the less obtrusive PPG signal, the proposed technique relies on simple feature extraction and classification tools, thus making it a promising alternative to the ECG based techniques for implementation on the personal healthcare devices.