How HRV and EEG Data Led to a Nightmare Alarm Clock Idea

Randy Sargent used a Polar H7 heart rate monitor, Hexoskin Shirt and Zeo EEG to feed his data into the fluxtream app. He then analyzed the data using spectrograms like you would see in audio tools like Audacity. He also made use of what used to be iPython Notebook and is now known as Project Jupyter. One of this most interesting findings from his QS research was that REM sleep appears to be stressful based on heart rate variability data. Now he is working on a nightmare alarm clock. This is a device that could detect nightmares and wake the person sleeping up when one is occurring.

Via Quantified Self:

In this talk, Randy Sargent shows how he used a spectrogram, a tool mostly used for audio, to better understand his own biometric data. A spectrogram was preferable to a line graph for its ability to visualize a large number of data points. As Randy points out, an eeg sensor can produce 100 million data points per day. It is unusual for a person to wear an eeg  sensor for that long, but Randy used the spectrogram on his heart rate variability data that was captured during a night of sleep. In the video, you’ll see an interesting pattern that he discovered that occurs during his REM sleep.


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