title: Extracting respiratory information from seismocardiogram signals acquired on the chest using a miniature accelerometer.
abstract: Seismocardiography (SCG) is a non-invasive measurement of the vibrations of the chest caused by the heartbeat. SCG signals can be measured using a miniature accelerometer attached to the chest, and are thus well-suited for unobtrusive and long-term patient monitoring. Additionally, SCG contains information relating to both cardiovascular and respiratory systems. In this work, algorithms were developed for extracting three respiration-dependent features of the SCG signal: intensity modulation, timing interval changes within each heartbeat, and timing interval changes between successive heartbeats. Simultaneously with a reference respiration belt, SCG signals were measured from 20 healthy subjects and a respiration rate was estimated using each of the three SCG features and the reference signal. The agreement between each of the three accelerometer-derived respiration rate measurements was computed with respect to the respiration rate derived from the reference respiration belt. The respiration rate obtained from the intensity modulation in the SCG signal was found to be in closest agreement with the respiration rate obtained from the reference respiration belt: the bias was found to be 0.06 breaths per minute with a 95% confidence interval of -0.99 to 1.11 breaths per minute. The limits of agreement between the respiration rates estimated using SCG (intensity modulation) and the reference were within the clinically relevant ranges given in existing literature, demonstrating that SCG could be used for both cardiovascular and respiratory monitoring. Furthermore, phases of each of the three SCG parameters were investigated at four instances of a respiration cycle-start inspiration, peak inspiration, start expiration, and peak expiration-and during breath hold (apnea). The phases of the three SCG parameters observed during the respiration cycle were congruent with existing literature and physiologically expected trends.
authors.affiliation: Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA. keya.pandia@stanfordalumni.org
authors.forename: Keya
authors.initials: K
authors.lastname: Pandia
authors.forename: Omer T
authors.initials: OT
authors.lastname: Inan
authors.forename: Gregory T A
authors.initials: GT
authors.lastname: Kovacs
authors.forename: Laurent
authors.initials: L
authors.lastname: Giovangrandi
mesh_terms: D061725:Accelerometry / Q000295:instrumentation*; D000328:Adult; D001049:Apnea / Q000503:physiopathology; D001146:Arrhythmia, Sinus / Q000503:physiopathology; D005260:Female; D006334:Heart Function Tests / Q000295:instrumentation*; D006339:Heart Rate; D006801:Humans; D007702:Kinetocardiography; D008297:Male; D008875:Middle Aged; D008904:Miniaturization / Q000295:instrumentation*; D011786:Quality Control; D012119:Respiration; D056152:Respiratory Rate; D012815:Signal Processing, Computer-Assisted; D013909:Thorax; D013997:Time Factors; D014732:Vibration; D055815:Young Adult