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Objective. Conduction-induced heart failure in patients with left bundle branch block (LBBB) can benefit from cardiac resynchronization therapy (CRT). However, some patients are non-responders to the therapy with one contributing factor being poor optimization of the atrioventricular (AV) pacing delay. In this study, we have investigated the pacing-induced changes in the seismocardiogram (SCG). Approach. 14 patients with heart failure, LBBB, and CRT were included. SCG was recorded with pacing turned on and off. Based on a mean SCG heartbeat from each patient, fiducial points were annotated, and cardiac timing intervals (CTI) and amplitudes were derived. These were compared between the CRT group and a group of healthy normal subjects ( n = 14). Echocardiography was also used to derive CTI. Intervals derived from the SCG and echocardiogram were correlated. Main results. The isovolumetric contraction time (IVCT) derived from SCG was significantly shorter in the CRT group when the pacemaker was turned on (63.2-52.6 ms, p = 0.027). The first peak-to-peak amplitude in the systolic complex was significantly larger with the pacemaker turned on ( p = 0.002), as well as the ∣max-min∣ amplitude in the systolic complex ( p = 0.003). Isovolumetric relaxation time and left ventricular ejection time (LVET) were not significantly different between pacemaker settings. Compared to normal subjects, IVCT was significantly prolonged with the pacemaker turned off. All amplitudes were significantly larger in the healthy subject group. IVCT and LVET derived from SCG were significantly correlated to the echocardiogram. Significance. IVCT shortened and SCG amplitudes increased in response to CRT, indicating a more efficient ventricular contraction. This demonstrates the possibility to detect cardio-mechanic changes in response to treatment with the SCG. However, for the patients the systolic part of the SCG was abnormal and difficult to characterize, raising concerns about the correct interpretation of the SCG., (© 2022 Institute of Physics and Engineering in Medicine.)

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., (Creative Commons Attribution license.)

Objective: Seismocardiography is the measurement of vibration waves caused by the beating heart with accelerometer(s) placed on the chest. Investigating the nature and the behavior of these vibration waves, by comparing measurements from multiple sites, would help to understand the heart's mechanical contraction activity., Approach: Using newly designed multichannel seismocardiogram equipment, it was possible to investigate the vibration waves with 16 three-axis sensors. The equipment performed well with highly precise synchronization rate over 10 min, linear frequency response and high signal quality. The vibration waves were analyzed using the sagittal axis, a single cardiac cycle and focusing on four fiducial points. Two of the fiducial point where the negative and positive peaks associated with aorta valve opening, along with peaks associated with aorta valve closing., Main Results: The respective average centers of mass of the four fiducial points in 13 subjects were at (frontal axis: 35 mm, vertical axis: 5 mm), (31, 6), (26, 24), and (4, -2), relative to the Xiphoid Process. Similar patterns among the subjects were identified for the propagation of the waves across the chest for the four fiducial points., Significance: The multichannel seismocardiogram equipment successfully revealed a general pattern present in chest surface vibration maps.

In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed.

Digital stethoscopes offer new opportunities for computerized analysis of heart sounds. Segmentation of heart sound recordings into periods related to the first and second heart sound (S1 and S2) is fundamental in the analysis process. However, segmentation of heart sounds recorded with handheld stethoscopes in clinical environments is often complicated by background noise. A duration-dependent hidden Markov model (DHMM) is proposed for robust segmentation of heart sounds. The DHMM identifies the most likely sequence of physiological heart sounds, based on duration of the events, the amplitude of the signal envelope and a predefined model structure. The DHMM model was developed and tested with heart sounds recorded bedside with a commercially available handheld stethoscope from a population of patients referred for coronary arterioangiography. The DHMM identified 890 S1 and S2 sounds out of 901 which corresponds to 98.8% (CI: 97.8-99.3%) sensitivity in 73 test patients and 13 misplaced sounds out of 903 identified sounds which corresponds to 98.6% (CI: 97.6-99.1%) positive predictivity. These results indicate that the DHMM is an appropriate model of the heart cycle and suitable for segmentation of clinically recorded heart sounds.