The heart rate data obtained from the Apple Watch ECG will be analyzed by visualizing its trends through the slope of 1/f fluctuations and time series, as well as using the Lorenz plot method.
Additionally, sentiment analysis will be performed to provide insights into your emotional state, such as whether you are relaxed or focused, based on situations like being at home or at work.
- In nature, light with wavelengths, music (such as Mozart or songs by singers), electrical signals, moving butterflies and fish, wind, sunlight filtering through trees, the sound of a flowing river, and flames all exhibit 1/f fluctuations.
- Similarly, the human heart rate rhythm also follows 1/f fluctuations as it carries out daily life activities. By regularly collecting data in the morning, afternoon, and evening with the readily available Apple Watch, accumulating heart rate data is highly meaningful for analysis.
- By checking the slope of 1/f fluctuations and the Lorenz plot graph in daily measurements, and visually grasping the slope of 1/f fluctuations, heart rate during measurement, and resting heart rate in time series through numbers and graphs, we can gain a closer understanding of the heart's function.
- By monitoring the changes in values and graphs, the mechanism that encourages maintaining health is called biofeedback, and it is recognized as an effective healthcare method.
- To improve the accuracy of the analysis, let's capture a clean heart rate waveform.
- Fix your arm on a table or your knee, and keep your fingers still for 30 seconds. After scrolling down to see the measurement results and tapping 'Done,' perform another 30-second measurement.
- A total of 4 repetitions, requiring 2 minutes of data, is necessary, and compared to other apps' 30-second measurements, HearTomo offers 4 times higher accuracy.
- Try to perform 4 consecutive measurements without leaving too much time between them.
- HearTomo development is based on the heart rate data analysis system patent 6430729.
- Mr. Osaka Motohisa conducted heart rate fluctuation analysis using his own calculation formula and an algorithm for spectral analysis of heart rate variability developed by Professor Ronald Berger from Johns Hopkins University Hospital.
- The heartbeat interval RRI data is converted to PSD (power spectral density), and the BPM (beats per minute) is subjected to FFT (fast Fourier transform) to obtain 1/f fluctuations.
- The slope of the 1/f fluctuations is then calculated.The vertical axis is the log10 of the PSD, and the horizontal axis is the log10 of the PSD frequency.
- -1 is a natural phenomenon in which various periodic components are "well" mixed together, and is considered to be pleasant
- One of the time domain indices of autonomic nervous activity is the heart beat interval RRI(n), which is the interval between the nth heart beat and the heart beat one beat before it.
- A Lorenz plot is a plot of RRI(n) values on the x-axis and RRI(n+1) values on the y-axis.
- The accumulated data is learned and updated daily,and emotion estimation suited to each individual is performed.
- The power value obtained from the spectrum analysis of heart rate variability is integrated over a frequency band of 0.04 to 0.15Hz to produce LF (low frequency), which mainly contains sympathetic nerve components.The power value obtained from the spectrum analysis of heart rate variability is integrated over a frequency band of 0.15 to 0.4Hz to produce HF (high frequency), which contains parasympathetic nerve components.
- The activity of the sympathetic and parasympathetic nerves is classified into four states: "both nerves high", "only sympathetic nerves high", "only parasympathetic nerves high", and "both nerves low", and emotion estimation is performed as the "voice of the heart".
- The red line represents the heart rate at rest, while the blue line represents the heart rate measured by HearTomo.
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Trend
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Emotion
Heartbeat trend
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2 situations(Home & Work)
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