Pulse plethysmograph signal analysis method for classification of heart diseases using novel local spectral ternary patterns. Issue 8 (10th April 2022)

• Record Type: Journal Article
• Authors: Aziz, Sumair; Khan, Muhammad Umar; Iqtidar, Khushbakht; Ali, Sadaqat; Remete, Ancuta Nicoleta; Javid, Muhammad Arshad
• Is Part Of: Expert systems. Volume 39:Issue 8(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-04-10
• Subjects: biomedical signal analysis -- cardiac disorders -- computer‐aided diagnosis -- empirical mode decomposition -- feature extraction -- local spectral ternary pattern (LSTP) -- pulse plethysmograph (PuPG) -- support vector machine; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.13011
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Cardiac disorders are one of the prime reasons for an increasing global death rate. Reliable and efficient diagnosis procedures are imperative to minimize the risk posed by heart disorders. Computer‐aided diagnosis, based on machine learning and biomedical signal analysis, has recently been adopted by researchers to accurately predict cardiac ailments. Multi‐channel Electrocardiogram signals are mostly used in scientific literature as an indicator to diagnose cardiac disorders. Recently pulse plethysmograph (PuPG) signal got attention as an evolving biosignal and promising diagnostic tool to detect heart disorders since it has a simple sensor with low cost, non‐invasive, reliable, and easy to handle technology. This article proposes a computer‐aided diagnosis system to detect Myocardial Infarction, Dilated Cardiomyopathy, and Hypertension from PuPG signals. Raw PuPG signal is first preprocessed through empirical mode decomposition (EMD) by removing the redundant and useless information content. Then, highly discriminative features are extracted from preprocessed PuPG signal through novel local spectral ternary patterns (LSTP). Extracted LSTPs are input to a variety of classification methods such as support vector machines (SVM), K‐nearest neighbours, decision tree, and so on. SVM with cubic kernel yielded the best classification performance of 98.4% accuracy, 96.7% sensitivity, and 99.6% specificity with 10‐fold cross‐validation. The proposed framework was trained and tested on a self‐collected PuPG signals database of heart disorders. A comparison with previous studies and other feature descriptors shows the superiority of the proposed system. This research provides better insights into the contributions of PuPG signals towards reliable detection of heart disorder through low‐cost and non‐invasive means.