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Related Concept Videos

Equipments Used To Measure Blood Pressure01:30

Equipments Used To Measure Blood Pressure

Direct Method
This invasive approach involves cannulating a peripheral artery. During each cardiac contraction, pressure generates mechanical motion within the catheter, transmitted through rigid, fluid-filled tubing to a transducer. This transducer converts mechanical motion into electrical signals displayed as waveforms on a monitor. An automatic flushing system prevents blood backflow. Due to the potential risk of unexpected arterial blood loss, this method is primarily used in intensive...
Assessing Blood pressure using a doppler ultrasound01:19

Assessing Blood pressure using a doppler ultrasound

To obtain accurate blood pressure measurements in clinical settings, especially when traditional methods are insufficient, healthcare professionals utilize the Doppler ultrasound technique. This method uses high-frequency sound waves to detect blood flow within the arteries, which is crucial for patients with conditions that complicate circulatory system assessment.
Pre-Procedural Guidelines for Doppler Ultrasound Blood Pressure Assessment:
Preparation of Equipment:
Heart Sounds01:15

Heart Sounds

Heart sounds are generated by the turbulence in blood flow due to the closing of heart valves. These sounds are best perceived slightly away from the valves, where the blood flow disseminates the sound.
Auscultation is the process of listening to these internal body sounds using a stethoscope. The heart produces four types of sounds, but only two—S1 and S2—can usually be heard with a stethoscope.
S1, also known as the "lub" sound, is caused by the closure of atrioventricular (A-V) valves at the...
Physical Assessment of the Respiratory Tract IV: Auscultation01:28

Physical Assessment of the Respiratory Tract IV: Auscultation

Auscultation is a crucial component of the physical assessment of the respiratory tract. It offers valuable insights into airflow through the bronchial tree and potential lung obstructions. This process involves careful listening to breath, voice, and adventitious sounds, which can reveal a wealth of information about a patient's respiratory health.
Breath Sounds
Breath sounds are categorized into vesicular, bronchovesicular, and bronchial.
Respiratory System Abnormal Finding II: Palpation and Auscultation01:31

Respiratory System Abnormal Finding II: Palpation and Auscultation

In assessing respiratory abnormalities, palpation and auscultation are critical tools for detecting and interpreting various pathophysiological changes. These techniques provide insight into underlying disorders by evaluating tactile sensations and sounds produced by the respiratory system.
Palpation Findings
During a respiratory assessment, palpation can reveal several vital abnormalities:

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Related Experiment Video

Updated: Jun 18, 2026

Behavioral Determination of Stimulus Pair Discrimination of Auditory Acoustic and Electrical Stimuli Using a Classical Conditioning and Heart-rate Approach
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Detection of Polyphonic Alarm Sounds From Medical Devices Using Frequency-Enhanced Deep Learning: Simulation Study.

Kazumasa Kishimoto1,2,3, Tadamasa Takemura4, Osamu Sugiyama5

  • 1Graduate School of Informatics, Kyoto University, 54 Kawara-cho, Shogoin, Sakyo-ku, Kyoto, 591-8022, Japan, 81 75-366-7701.

JMIR Medical Informatics
|November 12, 2025
PubMed
Summary
This summary is machine-generated.

This study developed an accurate AI system to identify medical device alarm sounds in noisy hospital settings, preventing missed alerts. The system uses advanced neural networks to classify sounds, improving patient safety by ensuring timely staff notification.

Keywords:
alarm sounddeep learningnotificationspolyphonic soundsound event detection

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence
  • Signal Processing

Background:

  • Many medical devices lack wireless connectivity, relying on auditory alarms for critical notifications.
  • Auditory alarms can be missed by staff in noisy environments or when they are in different locations.
  • Accurate automatic sound identification is crucial for non-networked medical devices to prevent missed critical alerts.

Purpose of the Study:

  • To design a method for classifying multiple medical device alarm sounds using a single microphone in a noisy hospital ward.
  • To develop a robust system capable of distinguishing various alarm sounds amidst ambient noise.

Main Methods:

  • Extracted features from 7 distinct alarm sounds using a mel filter bank.
  • Employed a hybrid convolutional neural network (CNN) and recurrent neural network (RNN) architecture for sound classification.
  • Evaluated the classifier's performance using simulated mixtures of alarm sounds and hospital ward noise at various signal-to-noise ratios.

Main Results:

  • The proposed CNN-RNN model achieved a high event-based F1-score of 0.967 at a 0 dB signal-to-noise ratio.
  • The best model demonstrated excellent precision (0.944) and recall (0.991) in identifying alarm sounds.
  • Classwise recall was exceptionally high, ranging from 0.990 to 1.000, indicating robust performance across different alarm types.

Conclusions:

  • The developed classifier exhibits high accuracy in detecting medical device alarm sounds.
  • Integration into an alarm sound detection system could enhance notifications from non-networked devices.
  • This technology has the potential to improve patient safety by ensuring timely staff response to critical events.