Cardiac monitoring of dogs via smartphone mechanocardiography: a feasibility study

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).

Heart Rate Variability Analysis on Electrocardiograms, Seismocardiograms and Gyrocardiograms on Healthy Volunteers

“…Heartbeat detection is based on the fact of quasi-periodic occurrence of waves associated with heartbeats [ 8 ]. The wave which indicates the heartbeat in seismocardiograms and gyrocardiograms is the aortic valve opening (AO) wave [ 28 , 30 ] also known as the wave in GCG signal morphology [ 8 ]. The AO wave occurs within 100 ms after the occurrence R wave in ECG signal [ 8 , 47 ].…”

Gyrocardiography: A Review of the Definition, History, Waveform Description, and Applications

“…New topics on gyrocardiography described in 2019 include performing cardiac monitoring of dogs via smarthphones [ 71 ], fetal heart rate (FHR) monitoring [ 72 ], investigating the influence of respiratory volume on the SCG and GCG waveforms [ 73 ] and evaluating the motion-based heart rate measurement [ 74 ]. Yang et al conducted the study on FHR estimation based on fetal cardiotocography sensor and the reported positive percent agreement (the equivalent of sensitivity/TPR when the reference is uncertain [ 75 ]) was 75.52% from GCG [ 72 ].…”

“…The GCG along with the SCG and BCG constitute the mechanocardiography (MCG) or vibrational cardiography (VCG). The first term was proposed by [ 24 , 49 , 55 , 58 , 60 , 64 , 65 , 71 , 73 , 77 , 88 ] and the second term was proposed by [ 9 , 73 , 79 , 80 ].…”

“…The advantages of GCG over other modalities are as follows: Small in size, accurate and readily available sensors [ 32 ], Only one sensor is required to perform the registration [ 32 ], No need for applying multiple electrodes [ 9 , 120 ], The signal is not affected by gravity [ 32 ], Signal registration is insensitive to the location of sensor relative to the heart [ 32 ], The possibility of: distinguishing the systolic and diastolic phases for analyses of left ventricular performance [ 80 , 121 ], embedding the sensor into smart clothes [ 122 ], performing the signal acquisition using sensors embedded in smartphones [ 64 , 71 , 123 ], Providing the opportunity to acquire respiratory movements for further analysis [ 69 , 113 ], High correlations with seismocardiograms [ 8 ], which are described since 1957 [ 30 , 92 ], Better noise rejection performance than in seismocardiograms [ 8 , 105 ], Better performance in PEP estimation than in SCG [ 112 ]. …”

“…The possibility of: distinguishing the systolic and diastolic phases for analyses of left ventricular performance [ 80 , 121 ], embedding the sensor into smart clothes [ 122 ], performing the signal acquisition using sensors embedded in smartphones [ 64 , 71 , 123 ], …”