Abstract
Building upon the findings from the field of automated recognition of respiratory sound patterns, we propose a wearable wireless sensor implementing on-board respiratory sound acquisition and classification, to enable continuous monitoring of symptoms, such as asthmatic wheezing. Low-power consumption of such a sensor is required in order to achieve long autonomy. Considering that the power consumption of its radio is kept minimal if transmitting only upon (rare) occurrences of wheezing, we focus on optimizing the power consumption of the digital signal processor (DSP). Based on a comprehensive review of asthmatic wheeze detection algorithms, we analyze the computational complexity of common features drawn from short-time Fourier transform (STFT) and decision tree classification. Four algorithms were implemented on a low-power TMS320C5505 DSP. Their classification accuracies were evaluated on a dataset of prerecorded respiratory sounds in two operating scenarios of different detection fidelities. The execution times of all algorithms were measured. The best classification accuracy of over 92%, while occupying only 2.6% of the DSP's processing time, is obtained for the algorithm featuring the time-frequency tracking of shapes of crests originating from wheezing, with spectral features modeled using energy.
Highlights
Asthma is one of the most common chronic diseases, affecting more than 300 million patients worldwide
Considering that the radio power consumption is kept minimal if transmitting only upon occurrences of wheezing, we focus on optimizing the power consumption of the digital signal processor (DSP)
By featuring a clear inflection point, they enable the unambiguous setting of the classification parameter thresholds, which yield the combination of the highest true positive rate at the lowest false positive rate
Summary
Asthma is one of the most common chronic diseases, affecting more than 300 million patients worldwide. Long-term disease management is required in order to maintain the life quality of asthmatic patients and to prevent the progression of the disease. Management mainly consists of adherence to a prescribed medication plan and avoidance of asthmatic attack triggers. The occurrence of symptoms, such as “asthmatic wheezing” in respiratory sound, indicates a low level of control over the chronic disease [1]. Medical devices for the quantification of wheezing appeared on the market [2]. The current practice of long-term asthma management still lacks a low-cost and wearable sensing system to empower patients and caregivers to continuously track the intensity of symptoms on their own
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