Cardiac monitoring of dogs via smartphone mechanocardiography: a feasibility study

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http://hdl.handle.net/10138/301309

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BioMedical Engineering OnLine. 2019 Apr 23;18(1):47

Title: Cardiac monitoring of dogs via smartphone mechanocardiography: a feasibility study
Author: Lahdenoja, Olli; Hurnanen, Tero; Kaisti, Matti; Koskinen, Juho; Tuominen, Jarno; Vähä-Heikkilä, Matti; Parikka, Laura; Wiberg, Maria; Koivisto, Tero; Pänkäälä, Mikko
Publisher: BioMed Central
Date: 2019-04-23
URI: http://hdl.handle.net/10138/301309
Abstract: Abstract Background In the context of monitoring dogs, usually, accelerometers have been used to measure the dog’s movement activity. Here, we study another application of the accelerometers (and gyroscopes)—seismocardiography (SCG) and gyrocardiography (GCG)—to monitor the dog’s heart. Together, 3-axis SCG and 3-axis GCG constitute of 6-axis mechanocardiography (MCG), which is inbuilt to most modern smartphones. Thus, the objective of this study is to assess the feasibility of using a smartphone-only solution to studying dog’s heart. Methods A clinical trial (CT) was conducted at the University Small Animal Hospital, University of Helsinki, Finland. 14 dogs (3 breeds) including 18 measurements (about one half of all) where the dog’s status was such that it was still and not panting were further selected for the heart rate (HR) analysis (each signal with a duration of 1 min). The measurement device in the CT was a custom Holter monitor including synchronized 6-axis MCG and ECG. In addition, 16 dogs (9 breeds, one mixed-breed) were measured at home settings by the dog owners themselves using Sony Xperia Android smartphone sensor to further validate the applicability of the method. Results The developed algorithm was able to select 10 good-quality signals from the 18 CT measurements, and for 7 of these, the automated algorithm was able to detect HR with deviation below or equal to 5 bpm (compared to ECG). Further visual analysis verified that, for approximately half of the dogs, the signal quality at home environment was sufficient for HR extraction at least in some signal locations, while the motion artifacts due to dog’s movements are the main challenges of the method. Conclusion With improved data analysis techniques for managing noisy measurements, the proposed approach could be useful in home use. The advantage of the method is that it can operate as a stand-alone application without requiring any extra equipment (such as smart collar or ECG patch).
Subject: Seismocardiography
SCG
Gyrocardiography
GCG
Mechanocardiography
MCG
Electrocardiography
ECG
Smartphone
Dog
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