Department of Future Technologies, Faculty of Science and Engineering, University of Turku, Vesilinnantie 5, 20014, Turku, Finland.
Department of Equine and Small Animal Medicine, Faculty of Veterinary Medicine, University of Helsinki, PL 57 Koetilantie 2, 00014, Helsinki, Finland.
Biomed Eng Online. 2019 Apr 23;18(1):47. doi: 10.1186/s12938-019-0667-9.
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.
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.
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.
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).
在监测犬只的过程中，通常使用加速度计来测量犬只的运动活动。在这里，我们研究了加速度计（和陀螺仪）的另一种应用——心震描记术（SCG）和心旋描记术（GCG），以监测犬只的心脏。三轴 SCG 和三轴 GCG 共同构成了内置在大多数现代智能手机中的六轴力学心音图（MCG）。因此，本研究的目的是评估仅使用智能手机解决方案来研究犬只心脏的可行性。
在芬兰赫尔辛基大学小动物医院进行了一项临床研究（CT）。从总共 18 次测量中（约占一半），进一步选择了 14 只犬（3 个品种）的测量值，这些犬的状态是静止且不喘气的，以便进行心率（HR）分析（每个信号持续 1 分钟）。CT 中的测量设备是一个定制的 Holter 监测器，包括同步的六轴 MCG 和 ECG。此外，16 只犬（9 个品种，一个混血品种）由犬主人在家中使用索尼 Xperia Android 智能手机传感器进行测量，以进一步验证该方法的适用性。
所开发的算法能够从 18 次 CT 测量中选择 10 个高质量信号，对于其中 7 个信号，自动算法能够以低于或等于 5 bpm 的偏差检测到 HR（与 ECG 相比）。进一步的视觉分析验证了，对于大约一半的犬，在家环境中的信号质量至少在某些信号位置足以提取 HR，而由于犬只运动引起的运动伪影是该方法的主要挑战。
通过改进用于管理噪声测量的数据分析技术，该方法在家庭使用中可能很有用。该方法的优点是它可以作为独立的应用程序运行，而无需任何额外的设备（如智能项圈或 ECG 贴片）。
