Asthmatic Wheeze Detection From Compressively Sensed Respiratory Sound Spectra

Abstract

Quantification of wheezing by a sensor system consisting of a wearable wireless acoustic sensor and smartphone performing respiratory sound classification may contribute to the diagnosis, long-term control, and lowering treatment costs of asthma. In such battery-powered sensor system, compressive sensing (CS) was verified as a method for simultaneously cutting down power cost of signal acquisition, compression, and communication on the wearable sensor. Matching real-time CS reconstruction algorithms, such as orthogonal matching pursuit (OMP), have been demonstrated on the smartphone. However, their lossy performance limits the accuracy of wheeze detection from CS-recovered short-term Fourier spectra (STFT), when using existing respiratory sound classification algorithms. Thus, here we present a novel, robust algorithm tailored specifically for wheeze detection from the CS-recovered STFT. The proposed algorithm identifies occurrence and tracks multiple individual wheeze frequency lines using hidden Markov model. The algorithm yields 89.34% of sensitivity, 96.28% of specificity, and 94.91% of accuracy on Nyquist-rate sampled respiratory sounds STFT. It enables for less than 2% loss of classification accuracy when operating over STFT reconstructed by OMP, at the signal compression ratio of up to 4 $\times$ (classification from only 25% signal samples). It features execution speed comparable to referent algorithms, and offers good prospects for parallelism.

System-Level Power Consumption Analysis of the Wearable Asthmatic Wheeze Quantification

“…Detailed reviews given in [12,18,20] report classification performance ranging on average from 90 to 95% of sensitivity and specificity. Building upon the existing analysis of low-power DSP implementable wheeze detection algorithms [12,21], we fix the algorithm choice to the STFT frequency line tracking algorithms based on either the empirical rules [12] or the hidden Markov model (HMM) [22,23]. Both yield similar, representative classification performance of 87-89% sensitivity and 93-96% specificity.…”

“…Also, we provide generalized guidelines on hardware component architectures best fitting the application. The analysis builds up upon our extensive prior research on novel energy-efficient signal acquisition and wireless transport schemes [30], design of specialized low-power wheeze recognition algorithms suitable for running onboard energy-constrained devices (sensor node and smartphone) [12,23], and verification of all subsystems on several hardware laboratory prototypes [12,22,31,32].…”

“…By knowing the subsampling pattern, the CS decoder subsystem block obtains a reconstruction of the compressible STFT spectra from the CS-encoded signal [30,32]. Finally, a robust hidden Markov model-(HMM-) based frequency line tracking algorithm [23] implemented on a smartphone [22] enables for wheeze detection from reconstructed spectra at less than 5% loss of classification accuracy.…”

“…In the third scenario, Nyquist rate signal acquisition and respiratory sound classification are performed onboard the wearable sensor, such as in [13,16]. Here, we compare the processing burden of the DSP implementations of robust frequency line tracking based on HMM [22,23] Journal of Sensors frequency-tracking algorithm mimicking the nearest neighbor association previously presented in [12], analogous to [10]. The sensor periodically reports the classification result to the smartphone.…”

Detection of Different Brain Diseases from EEG Signals Using Hidden Markov Model

“…HMM has made its mark in different medical, biology and rehabilitation fields; for example, identifying movement states of Parkinsonian patients [5], personal identification system [6], functional brain networks [7], quantification of wheezing for respiratory sound classification [8], single-molecule data analysis in time series which accommodates complications such as drift [9]. Septic shock of critical care patient causes multiple organ failure and eventual death.…”