Abstract
The analysis of cardiac signals is still regarded as attractive by both the academic community and industry because it helps physicians in detecting abnormalities and improving the diagnosis and therapy of diseases. Electrocardiographic signal processing for detecting irregularities related to the occurrence of low-amplitude waveforms inside the cardiac signal has a considerable workload as cardiac signals are heavily contaminated by noise and other artifacts. This paper presents an effective approach for the detection of ventricular late potential occurrences which are considered as markers of sudden cardiac death risk. Three stages characterize the implemented method which performs a beat-to-beat processing of high-resolution electrocardiograms (HR-ECG). Fifteen lead HR-ECG signals are filtered and denoised for the improvement of signal-to-noise ratio. Five features were then extracted and used as inputs of a classifier based on a machine learning approach. For the performance evaluation of the proposed method, a HR-ECG database consisting of real ventricular late potential (VLP)-negative and semi-simulated VLP-positive patterns was used. Experimental results show that the implemented system reaches satisfactory performance in terms of sensitivity, specificity accuracy, and positive predictivity; in fact, the respective values equal to 98.33%, 98.36%, 98.35%, and 98.52% were achieved.
Highlights
The use of science and technology in medicine is an evolving field that addresses complex medical issues by integrating the latest advancements in the fields of biology, chemistry, and physics with engineering and medicine for solving challenges in human health [1,2,3].In medical signal processing, the accurate diagnosis and/or assessment of a disease depends on both signal acquisition and interpretation
For the performance evaluation of the implemented computer-aided detection (CAD) system, the adopted high-resolution electrocardiograms (HR-electrocardiogram signal (ECG)) database was divided into a training set, including 45 HR-ECG signals with and 45 without ventricular late potential (VLP), as well as a test set consisting of 15 records without and 15 with VLPs
Since each HR-ECG is recorded adopting 15 leads and is 2 min long, 450 cardiac tracings for a total of about 54,000 heartbeats have been tested for the system evaluation
Summary
The accurate diagnosis and/or assessment of a disease depends on both signal acquisition and interpretation. Medical signal interpretation processes can benefit from computer technology [4,5,6]. It can help clinicians to screen abnormalities and the risks associated with them and contribute to the diagnosis of clinical signs for the purpose of recognizing the nature and cause of the pathological event [7,8]. Computer-aided interpretation is useful when either a large amount of data has to be analyzed or the observation time is long, as is the case for the diagnosis of cardiovascular diseases. Since the state of the heart is related to the shape of the electrocardiogram signal (ECG) and to the heart rate, cardiologists consider the ECG to be a representative signal of cardiac physiology which is useful for detecting cardiac pathologies
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