18480042. ELECTROCARDIOGRAM-BASED DIAGNOSIS MODEL LEARNING DEVICE, ELECTROCARDIOGRAM-BASED DIAGNOSIS MODEL LEARNING METHOD, AND STORAGE MEDIUM simplified abstract (NEC Corporation)
Contents
- 1 ELECTROCARDIOGRAM-BASED DIAGNOSIS MODEL LEARNING DEVICE, ELECTROCARDIOGRAM-BASED DIAGNOSIS MODEL LEARNING METHOD, AND STORAGE MEDIUM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ELECTROCARDIOGRAM-BASED DIAGNOSIS MODEL LEARNING DEVICE, ELECTROCARDIOGRAM-BASED DIAGNOSIS MODEL LEARNING METHOD, AND STORAGE MEDIUM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
ELECTROCARDIOGRAM-BASED DIAGNOSIS MODEL LEARNING DEVICE, ELECTROCARDIOGRAM-BASED DIAGNOSIS MODEL LEARNING METHOD, AND STORAGE MEDIUM
Organization Name
Inventor(s)
Kosuke Nishihara of Tokyo (JP)
ELECTROCARDIOGRAM-BASED DIAGNOSIS MODEL LEARNING DEVICE, ELECTROCARDIOGRAM-BASED DIAGNOSIS MODEL LEARNING METHOD, AND STORAGE MEDIUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18480042 titled 'ELECTROCARDIOGRAM-BASED DIAGNOSIS MODEL LEARNING DEVICE, ELECTROCARDIOGRAM-BASED DIAGNOSIS MODEL LEARNING METHOD, AND STORAGE MEDIUM
Simplified Explanation
The learning device described in the patent application includes a first acquisition means for acquiring a partial waveform of electrocardiogram data, a second acquisition means for acquiring an attention interval in the sequential waveform of the electrocardiogram data, and a learning means for training a model to diagnose a target disease based on the acquired data.
- The first acquisition means acquires a partial waveform of electrocardiogram data.
- The second acquisition means acquires an attention interval in the sequential waveform of the electrocardiogram data.
- The learning means trains a model to diagnose a target disease based on the partial waveform and the attention interval.
Potential Applications
This technology could be applied in medical devices for diagnosing cardiovascular diseases based on electrocardiogram data.
Problems Solved
This technology helps in accurately diagnosing target diseases by analyzing specific intervals in electrocardiogram data.
Benefits
- Improved accuracy in diagnosing target diseases - Efficient use of electrocardiogram data for medical diagnosis
Potential Commercial Applications
"Advanced Cardiovascular Disease Diagnosis Technology" could be used in medical devices for early detection and monitoring of cardiovascular diseases.
Possible Prior Art
One possible prior art could be the use of machine learning algorithms in medical devices for diagnosing diseases based on physiological data.
Unanswered Questions
How does the learning device handle variations in electrocardiogram data from different subjects?
The patent abstract does not provide information on how the learning device accounts for individual variations in electrocardiogram data.
What is the accuracy rate of the model trained by the learning device in diagnosing the target disease?
The abstract does not mention the accuracy rate of the model trained by the learning device for diagnosing the target disease.
Original Abstract Submitted
A learning device X mainly includes a first acquisition means X, a second acquisition means X, and a learning means X. The first acquisition means X acquires a partial waveform of electrocardiogram data regarding an electrocardiogram of a subject. The second acquisition means X acquires an attention interval, which is used as a basis for a diagnosis of a target disease, in a sequential waveform of the electrocardiogram data. The learning means X trains a model configured to diagnose the target disease, based on the partial waveform and the attention interval.
