Seismocardiography (SCG) is a non-invasive measurement of the vibrations of the chest caused by the heartbeat. SCG signals can be measured using a miniature accelerometer attached to the chest, and are thus well-suited for unobtrusive and long-term patient monitoring. Additionally, SCG contains information relating to both cardiovascular and respiratory systems. In this work, algorithms were developed for extracting three respiration-dependent features of the SCG signal: intensity modulation, timing interval changes within each heartbeat, and timing interval changes between successive heartbeats. Simultaneously with a reference respiration belt, SCG signals were measured from 20 healthy subjects and a respiration rate was estimated using each of the three SCG features and the reference signal. The agreement between each of the three accelerometer-derived respiration rate measurements was computed with respect to the respiration rate derived from the reference respiration belt. The respiration rate obtained from the intensity modulation in the SCG signal was found to be in closest agreement with the respiration rate obtained from the reference respiration belt: the bias was found to be 0.06 breaths per minute with a 95% confidence interval of -0.99 to 1.11 breaths per minute. The limits of agreement between the respiration rates estimated using SCG (intensity modulation) and the reference were within the clinically relevant ranges given in existing literature, demonstrating that SCG could be used for both cardiovascular and respiratory monitoring. Furthermore, phases of each of the three SCG parameters were investigated at four instances of a respiration cycle-start inspiration, peak inspiration, start expiration, and peak expiration-and during breath hold (apnea). The phases of the three SCG parameters observed during the respiration cycle were congruent with existing literature and physiologically expected trends.
This paper presents a method of extracting primary heart sound signals from chest-worn accelerometer data in the presence of motion artifacts. The proposed method outperforms noise removal techniques such as wavelet denoising and adaptive filtering. Results from six subjects show a primary heart signal detection rate of 99.36% with a false positive rate of 1.3%.
Ballistocardiography and seismocardiography are both non-invasive mechanical measurements of the vibrations of the body in response to the heartbeat. The ballistocardiogram (BCG) signal represents the movements of the whole body in response to cardiac ejection of blood into the vasculature; the seismocardiogram (SCG) corresponds to local vibrations of the chest wall associated with sub-audible tissue and blood movement and audio frequency heart-valve closure dynamics. This paper focuses on methods for quantifying "signal consistency"--a quantitative measure of how morphologically similar each heartbeat in a patient's recording is compared to the ensemble average taken over the recording. Before comparing each beat to the average, known physiological sources of inconsistency--such as respiratory amplitude and timing variability--are removed, then the remaining inconsistency is quantified. Previously described methods for BCG signals are expanded to fit the high-frequency (> 20 Hz) components of the SCG. The use of this method in future work could help enable proactive management of heart disease in extra-clinical settings.
The seismocardiogram (SCG) signal traditionally measured using a chest-mounted accelerometer contains low-frequency (0-100 Hz) cardiac vibrations that can be used to derive diagnostically relevant information about cardiovascular and cardiopulmonary health. This work is aimed at investigating the effects of respiration on the frequency domain characteristics of SCG signals measured from 18 healthy subjects. Toward this end, the 0-100 Hz SCG signal bandwidth of interest was sub-divided into 5 Hz and 10 Hz frequency bins to compare the spectral energy in corresponding frequency bins of the SCG signal measured during three key conditions of respiration--inspiration, expiration, and apnea. Statistically significant differences were observed between the power in ensemble averaged inspiratory and expiratory SCG beats and between ensemble averaged inspiratory and apneaic beats across the 18 subjects for multiple frequency bins in the 10-40 Hz frequency range. Accordingly, the spectral analysis methods described in this paper could provide complementary and improved classification of respiratory modulations in the SCG signal over and above time-domain SCG analysis methods.
Chest-worn accelerometers have been shown to detect acoustic and mechanical signals corresponding to cardiovascular activity. This paper aims at investigating and characterizing two different components of chest acceleration (seismocardiogram) along two orthogonal axes: firstly, the sub-10 Hz ballistic signal components dominant in the vertical axis and secondly, the 10-50 Hz acoustic signal components more dominantly expressed in the radial axis. Acceleration signals from five subjects in response to a valsalva maneuver were measured. Correlations of features from the two above acceleration components were computed with respect to reference measurements of stroke volume and pulse pressure obtained with a Finapres continuous blood pressure system. The peak amplitude of the vertical ballistic and radial acoustic signal components were found to correlate well with stroke volume (R=0.78 and 0.83, for vertical ballistic and radial acoustic, respectively). Comparable correlations were found between beat RMS power (R=0.77 and 0.83) and stroke volume. Similarly, correlations were also observed between pulse pressure and peak amplitude (R=0.74 and 0.86) and the beat RMS power (R=0.74 and 0.86).
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