Key result
A fuzzy logic-based respiration monitoring algorithm successfully categorized respiratory signals into respiration, body motion, and apnea using the MIT physiology database and in-house measurements.
A novel fuzzy logic-based algorithm demonstrates capability in estimating respiration rate and detecting apnea episodes.
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May aid wearable apnea detection research; leaves open clinical validation before any practice consideration.
This paper presents a fuzzy logic-based respiration monitoring algorithm that is capable of providing accurate respiration rate and detecting apnea episodes. The proposed algorithm employs several signal processing techniques to extract useful features that signify different respiratory behaviors. We implement a fuzzy logic-based system that examines the extracted respiratory signal features and categorizes the respiratory signals into respiration, body motion, and apnea. The performance of the underlying algorithm is validated using both the MIT physiology database and in-house respiration measurements.
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Mudvari et al. (2017) studied Apnea. Fuzzy logic-based respiration monitoring algorithm was evaluated on Respiration rate and apnea detection. A fuzzy logic-based respiration monitoring algorithm successfully categorized respiratory signals into respiration, body motion, and apnea using the MIT physiology database and in-house measurements.
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