Spectral analysis of heart sounds associated with coronary artery disease

冠状动脉疾病 心脏病学 舒张期 内科学 计算机辅助设计 医学 光谱分析 收缩 声音(地理) 血压 声学 物理 工程类 光谱学 量子力学 工程制图
作者
Bjarke Skogstad Larsen,Simon Winther,Louise Nissen,Axel Cosmus Pyndt Diederichsen,Morten Bøttcher,Johannes Struijk,Mads Græsbøll Christensen,Samuel Schmidt
出处
期刊:Physiological Measurement [IOP Publishing]
卷期号:42 (10): 105013-105013 被引量:2
标识
DOI:10.1088/1361-6579/ac2fb7
摘要

Objective. The aim of this study was to find spectral differences of diagnostic interest in heart sound recordings of patients with coronary artery disease (CAD) and healthy subjects.Approach. Heart sound recordings from three studies were pooled, and patients with clear diagnostic outcomes (positive: CAD and negative: Non-CAD) were selected for further analysis. Recordings from 1146 patients (191 CAD and 955 Non-CAD) were analyzed for spectral differences between the two groups using Welch's spectral density estimate. Frequency spectra were estimated for systole and diastole segments, and time-frequency spectra were estimated for first (S1) and second (S2) heart sound segments. An ANCOVA model with terms for diagnosis, age, gender, and body mass index was used to evaluate statistical significance of the diagnosis term for each time-frequency component.Main results. Diastole and systole segments of CAD patients showed increased energy at frequencies 20-120 Hz; furthermore, this difference was statistically significant for the diastole. CAD patients showed decreased energy for the mid-S1 and mid-S2 segments and conversely increased energy before and after the valve sounds. Both S1 and S2 segments showed regions of statistically significant difference in the time-frequency spectra.Significance. Results from analysis of the diastole support findings of increased low-frequency energy from previous studies. Time-frequency components of S1 and S2 sounds showed that these two segments likely contain heretofore untapped information for risk assessment of CAD using phonocardiography; this should be considered in future works. Further development of features that build on these findings could lead to improved acoustic detection of CAD.

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