Mechanocardiography detects improvement of systolic function caused by resynchronization pacing.

Objective.Cardiac resynchronization therapy (CRT) is commonly used to manage heart failure with dyssynchronous ventricular contraction. CRT pacing resynchronizes the ventricular contraction, while AAI (single-chamber atrial) pacing does not affect the dyssynchronous function. This study compared waveform characteristics during CRT and AAI pacing at similar pacing rates using seismocardiogram (SCG) and gyrocardiogram (GCG), collectively known as mechanocardiogram (MCG).Approach.We included 10 patients with heart failure with reduced ejection fraction and previously implanted CRT pacemakers. ECG and MCG recordings were taken during AAI and CRT pacing at a heart rate of 80 bpm. Waveform characteristics, including energy, vertical range (amplitude) during systole and early diastole, electromechanical systole (QS2) and left ventricular ejection time (LVET), were derived by considering 6 MCG axes and 3 MCG vectors across frequency ranges of >1 Hz, 20-90 Hz, 6-90 Hz and 1-20 Hz.Main results.Significant differences were observed between CRT and AAI pacing. CRT pacing consistently exhibited higher energy and vertical range during systole compared to AAI pacing (p< 0.05). However, QS2, LVET and waveform characteristics around aortic valve closure did not differ between the pacing modes. Optimal differences were observed in SCG-Y, GCG-X, and GCG-Y axes within the frequency range of 6-90 Hz.Significance.The results demonstrate significant differences in MCG waveforms, reflecting improved mechanical cardiac function during CRT. This information has potential implications for predicting the clinical response to CRT. Further research is needed to explore the differences in signal characteristics between responders and non-responders to CRT.

Unveiling the Relationships Between Seismocardiogram Signals, Physical Activity Types and Metabolic Equivalent of Task Scores.

OBJECTIVE: The diagnosis of metabolic syndrome and cardiovascular disorders can highly benefit from physical activity and energy expenditure assessment. In this study, we investigated the relationship between metabolic equivalent of task (MET) scores and seismocardiogram (SCG)-derived parameters. METHODS: We worked with the PAMAP2 dataset and focused on the 3-axial chest acceleration data. We first segmented the 3-axial SCG signals into respiration (0-1 Hz), cardiac vibrations (1-20 Hz) and heart sounds (20-40 Hz) components. Additionally, we investigated their combinations: 0-20 Hz, 1-40 Hz and 0-40 Hz. We then windowed each signal, and extracted time and frequency domain features from each window. Using the MET scores and activity types, we trained linear regression and random forest classification models first using 80-20% split, then with leave-one-subject-out cross-validation (LOSO-CV). Additionally, we investigated the significance of each feature and axis. RESULTS: For the 80-20% task, the best performing frequency bands were 0-1 Hz, 0-20 Hz, and 0-40 Hz, which yielded a (MET mean-squared-error, classification accuracy) pair of (0.354, 0.952), (0.367, 0.904), and (0.377, 0.914), respectively. When LOSO-CV was applied, we obtained (1.059, 0.865), (0.681, 0.868), and (0.804, 0.875) for each band, respectively. Additionally, our results revealed that the lateral axis provides the most critical information about cardiorespiratory effect of performed activities. CONCLUSION: Different SCG components can provide unique and substantial contributions to activity and energy expenditure assessment. SIGNIFICANCE: This framework can be leveraged in the design of wearable systems for monitoring the activity and energy expenditure levels, and understanding their relationship with underlying cardiorespiratory parameters.