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).

Keywords: Dog; ECG; Electrocardiography; GCG; Gyrocardiography; MCG; Mechanocardiography; SCG; Seismocardiography; Smartphone.

Fig. 1

The available measurement devices of the study. The devices used in the measurements: (1) our custom Holter monitor (on the left, used in CT-A), (2) AliveCor’s phone ECG and patch (in the middle, used in HM-C), and (3) a 3D printed mounting for Android device (used in HM-B, not the cover, on the right) for improved contact and to avoid the sliding of the phone

Fig. 2

Implementing smartphone-only measurement. The placement of the smartphone in home measurements (HM-B) was usually on the ridge (while the dog was standing or in prone position) or on either lateral side of the dog, while the dog was resting on the other side. The users of the data collection application were advised to avoid grasping the phone hard. During the clinical measurements (CT-A), the Holter device was wrapped/hold on the left lower lateral side, over the heart region, while dog was standing or lying on the right side

Fig. 3

Axis and measurement selection method. A flowchart of the measurement and axis selection method considering trial CT-A and an example of SNR calculation (on the right)

Fig. 4

Examples of each signal measured at home. A good-quality signal segment (of a length of 10 s) according to human expert’s visual inspection from each of the 16 home measurements (of HM-B). Only the band-pass filtered AccZ and the GyroY signal axes are shown. The weights of the dogs are also shown in parenthesis. The signals were captured with the Android device while placing the smartphone on either lateral side of the dog or on the ridge, while in rest in a side, prone, or in a standing position

Fig. 5

Example signal from 15 kg Wheaten Terrier. Time-aligned iPhone’s IMU signal (AccZ axis only shown below) and synchronized simultaneously captured ECG from AliveCor’s patch (above) converted from pdf output. It can be observed that the AO peaks in SCG match to the R peaks in ECG