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Related Concept Videos

Panic Disorder01:27

Panic Disorder

193
Panic disorder is an anxiety disorder characterized by recurrent and sudden minutes-long episodes of intense fear, known as panic attacks. These attacks may feel like heart attacks and often happen without warning or a specific cause. They can include symptoms such as rapid heart rate, shortness of breath, chest pain, trembling, sweating, dizziness, and a sense of helplessness. During a panic attack, individuals may feel as though they are experiencing a heart attack or are in a...
193
Pulse rhythm01:30

Pulse rhythm

972
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
972
Holter Monitor: 24-Hour Monitoring01:23

Holter Monitor: 24-Hour Monitoring

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Holter monitoring is a continuous electrocardiography (ECG) recording that tracks the heart's electrical activity over an extended period, generally 24 to 48 hours. This noninvasive diagnostic tool detects irregular heart rhythms that may not be captured during a standard ECG performed in a clinical setting.DeviceThe Holter monitor is a portable, small device connected to several electrodes on the patient's chest. These electrodes detect the heart's electrical signals and transmit them to the...
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Panic Attack Prediction Using Wearable Devices and Machine Learning: Development and Cohort Study.

Chan-Hen Tsai1,2, Pei-Chen Chen1, Ding-Shan Liu3

  • 1Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei City, Taiwan.

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|February 15, 2022
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Summary
This summary is machine-generated.

Predicting panic attacks (PAs) is possible using machine learning models that analyze physiological, psychological, and environmental data. This 7-day prediction model shows promising accuracy for personalized panic disorder (PD) management.

Keywords:
lifestylemachine learningpanic attackpanic disorderpredictionwearable device

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Area of Science:

  • Computational psychiatry
  • Digital health
  • Machine learning in healthcare

Background:

  • Panic attacks (PAs) are severe anxiety episodes with physical symptoms, causing frequent emergency visits and reduced quality of life.
  • A predictive model for PAs can aid in monitoring, control, and early intervention for recurrent PAs in panic disorder (PD).

Purpose of the Study:

  • To develop a 7-day prediction model for panic attacks (PAs).
  • To identify relationships between future PAs and physiological factors, anxiety/depression scores, and air quality index (AQI).

Main Methods:

  • 59 participants with PD provided data via smartwatches and mobile apps over 1 year.
  • Collected data included sleep, heart rate (HR), activity, anxiety/depression scores (BDI, BAI, STAI), and AQI.
  • Six machine learning algorithms were employed for data analysis.

Main Results:

  • Random forest model achieved the highest prediction rate for 7-day PAs.
  • Model accuracy ranged from 67.4% to 81.3% across different machine learning algorithms.
  • Key predictive variables included questionnaire scores (BAI, BDI, STAI) and physiological data (HR, sleep duration).

Conclusions:

  • Panic attacks can be predicted using a combination of questionnaire, physiological, and environmental data.
  • This approach supports personalized treatment strategies for panic disorder.