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%.
Algorithm design for low power platforms is constrained by memory and computational limitations, and realworld applications demand robust performance. This paper presents two algorithms that were designed with the view that simplicity can translate to robustness. The first algorithm processes electrocardiogram (ECG) signals to detect QRS complexes reliably in the presence of significant noise. The second algorithm is a low-cost approach to detecting seizure onset from electrocorticogram (ECoG) data. The ECG algorithm was implemented on a TI MSP430-based platform and the ECoG algorithm was implemented (in simulation) on a Cortex M3 based ultra-low power device.
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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