The cardiovascular diseases are major cause of mortality across the globe contributing 31% of global death burden . The electrocardiography (ECG) is used to detect abnormalities in the cardiac rhythms during the onset of cardiovascular problems , mostly diagnosed when damage has been already done. It is evident that rhythms of heart started to change long before the onset of disease. To trace such abnormalities in the cardiac rhythms well in time, a short and long-term ambulatory ECG (AECG) recording is required. Therefore, AECG monitoring is becoming vitally important for the early detection of abnormal events in home care or personalize care and clinical settings in order to prevent the onset cardiovascular disease [3, 4].
The conventional Holter monitor is a portable battery-operated device which is connected to the patient using electrodes. The recorded data is stored on analog or digital storage media in the device which is converted into the digital format for analysis .
One of the limitation of these device is that it is not easy to operate and need skilled professionals to extract data, remove artifacts and analyze the results. Another limitation is that the conventional holter monitors are not intelligent devices and can’t detect abnormal rhythms during recording period, for example if someone is experiencing abnormal cardiac event or having some symptoms. Moreover, the size of the holter monitor may create problems to sleep comfortably, delimit their physical activities and inconvenience to removal of device during bathing resulting in low diagnostic yield . Furthermore, the recorded data needs to transfer to the computer for analysis and adverse event detection which limits their ability for real time data monitoring.
No doubt the main inconvenience of HRV-based applications is the need for a heart rate monitor. Even when you have one, performing your daily measurement can be a burden (sensor needs to be wet and comfort is clearly an issue). Many sensor-based HRV smartphone apps are introduced to overcome this situation. They are easy to operate and less technical skills are required to operate, but ones need to buy a sensor upfront to use such applications.
Can smartphones provide the personalize care?
The most of the recent smartphones generation include both a camera and a light emitting diode, which can be used for reflection based bio-optical imaging. The technique is called photoplethysmography (PPG) which is used to detect changes in blood volume during a cardiac cycle, by illuminating the skin and measuring changes in light absorption. PPG has become a quite popular non-invasive method for extracting physiological measurements such as heart rate and oxygen saturation. However, many applications today focus simply on heart rate, and it is not clear from literature if HRV features can also be reliably extracted using a phone’s camera or not.
What HRV Explorer can do?
HRV Explorer is currently available in Google Play Store. Please download and give us your feedback for future improvements.
Note: HRV Explorer is sufficiently accurate. But it doesn’t meant as a replacement for professional medical equipment and qualified care. If you find any abnormality or have any concerns about your heart’s condition then please consult your physician or general practitioner.
 GBD 2013 Mortality and Causes of Death Collaborators, “Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013,” thelancet, vol. 385, 2015.
 M. A. García-González, J. Ramos-Castro, and M. Fernández-Chimeno, “A new index for the analysis of heart rate variability dynamics: Characterization and application,” Physiological Measurement, vol. 24, no. 4, p. 819, 2003.
 D. Jabaudon, J. Sztajzel, K. Sievert, T. Landis, and R. Sztajzel, “Usefulness of ambulatory 7-day ECG monitoring for the detection of atrial fibrillation and flutter after acute stroke and transient ischemic attack,” Stroke, vol. 35, no. 7, pp. 1647–1651, 2004.
 F. Miao, Y. Cheng, Y. He, Q. He, and Y. Li, “A Wearable Context-Aware ECG Monitoring System Integrated with Built-in Kinematic Sensors of the Smartphone,” Sensors (Basel, Switzerland), vol. 15, no. 5, pp. 11465–11484, 2015.
 S. Rajesh, Lorne, J., Gula, George, J., Klein, Allan, C., Skanes, Y. Raymond, and Andrew, D., Krahn, “Syncope: Review of Monitoring Modalities,” Current Cardiology Reviews, vol. 4, pp. 41–48, 2008.  Jonathan, E., and Martin Leahy. “Investigating a smartphone imaging unit for photoplethysmography.” Physiological Measurement 31.11 (2010): N79.