Lee et al.18) |
Blood pressure |
Proposed blood pressure estimation algorithm using the relationship between blood flow and electrocardiogram results |
Pulse wave velocity was strongly correlated with blood pressure |
Stronger correlation with diastolic blood pressure than systolic blood pressure, which directly related to pulse wave |
Prokhorov et al.19) |
Low-frequency oscillation of the pulse and blood flow (index [which may worsen during acute myocardial infarction and hypertension]) |
Developed a mobile app to measure 24-hour pulse and blood flow data to assess cardiovascular status in real time, continuously record photoplethysmogram signals from the finger, and monitor synchronization of pulse and vibration of blood flow |
The difference between the index s value calculated only using the photoplethysmogram and the index s value calculated on the electrocardiogram as well was less than 2% |
Earlobe data for photoplethysmograms will be added |
Ahn and Cho20) |
Heart sounds |
Machine learning technique assessed cardiovascular disease using heart sounds obtained via smartphone |
Age negatively related to the ease of the analysis of heart murmur |
Results confirmed only on android devices using limited data (no atrial fibrillation and diastolic murmur) |
Lee and Jung21) |
Heartrate |
Photoplethysmogram sensor measured heartrate and transmitted the RR interval* to a personal computer |
Statistically significant correlation with heartrate measurement equipment |
Further miniaturization and measurement on smartphone |
Fang et al.22) |
Electrocardiogram, heartrate, blood pressure, globin insulin, electroencephalogram, and so on |
Device measurement and patient selfmeasurement |
Designed a mobile early warning system that immediately alerts the patient's guardian, medical staff, and/or nearby hospital |
After collecting data, it is necessary to identify anomalies to further develop and implement the system |
Lee et al.23) |
Electrocardiogram, arterial pulse wave, pulse wave velocity, accelerated arterial pulse wave |
Monitored cardiovascular system via wrist device with data sent to smartphone via Bluetooth in real time |
Arterial pulse wave measurement error less than 1% |
Needs future verification with many other patients |
Villamil et al.24) |
Mobile electrocardiogram |
Calculated cardiovascular risk with electrocardiogram data transmitted to electronic medical records |
Cardiovascular risk assessed on android devices in low- and middle-income countries |
Needs verification with electrocardiogram measured using 3 leads |
Shyamkumar et al.25) |
Electrocardiogram, heartrate, blood pressure |
- Shirt sensor (e-bro) for men and bra sensor (e-bra) for women |
- Tracked chronic conditions related to autonomous nervous regulation of cardiac activity |
Needs an app to check the data using a wearable sensor that monitors breathing and blood oxygen levels |
- Transmits electrocardiogram, heartrate, and blood pressure data to personal computer and smartphone |
- Detected t-wave inversion |
Kim26) |
Aging index |
Detect and evaluate vascular stiffness in real time using a smartphone |
Vascular stiffness monitoring provided a preventive approach |
Needs verification by many other patients in more diverse environments |