Automatic respiratory phase detection for functional electrical stimulation synchronization

Abstract Introduction People with cervical or high thoracic spinal cord injury usually have respiratory muscle weakness. When transcutaneous functional electrical stimulation (TFES) synchronized with the patient’s natural breathing is applied to respiratory muscles, their strength and resistance are increased. In this work, we propose a novel method to perform an automatic synchronization, composed of a signal acquisition system and an algorithm that recognizes both respiratory cycle phases during quiet breathing. Methods The respiratory signal acquisition unit consists of a load cell attached to an elastic belt. The algorithm is based on statistical evaluation and linear approximation for detecting the beginning of both inhalation and exhalation phases. Ten volunteers remained steady, breathing quietly for one minute for signal acquisition. Results The system’s automatic detection of inspiratory events reached 87.5% of true positives, 6.7% of false negatives and 5.8% of false positives. Both hit and error ratios obtained in the detection of expiratory events reached 94.3% true positives, 4.9% false positives and 0.8% false negatives. Conclusion The developed algorithm can identify the respiratory phases properly and it can be used in future synchronized TFES applications whether the patient remains in a quasi-static position during treatment.

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Bibliographic Details
Main Authors: Costa,Taisa Daiana da, Nogueira-Neto,Guilherme Nunes, Nohama,Percy
Format: Digital revista
Language:English
Published: Sociedade Brasileira de Engenharia Biomédica 2018
Online Access:http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402018000300246
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Summary:Abstract Introduction People with cervical or high thoracic spinal cord injury usually have respiratory muscle weakness. When transcutaneous functional electrical stimulation (TFES) synchronized with the patient’s natural breathing is applied to respiratory muscles, their strength and resistance are increased. In this work, we propose a novel method to perform an automatic synchronization, composed of a signal acquisition system and an algorithm that recognizes both respiratory cycle phases during quiet breathing. Methods The respiratory signal acquisition unit consists of a load cell attached to an elastic belt. The algorithm is based on statistical evaluation and linear approximation for detecting the beginning of both inhalation and exhalation phases. Ten volunteers remained steady, breathing quietly for one minute for signal acquisition. Results The system’s automatic detection of inspiratory events reached 87.5% of true positives, 6.7% of false negatives and 5.8% of false positives. Both hit and error ratios obtained in the detection of expiratory events reached 94.3% true positives, 4.9% false positives and 0.8% false negatives. Conclusion The developed algorithm can identify the respiratory phases properly and it can be used in future synchronized TFES applications whether the patient remains in a quasi-static position during treatment.