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Electrodiagnosis (EDX) is a method of medical diagnosis that obtains information about diseases by passively recording the electrical activity of body parts (that is, their natural electrophysiology) or by measuring their response to external electrical stimuli (evoked potentials). The most widely used methods of recording spontaneous electrical activity are various forms of electrodiagnostic testing (electrography) such as electrocardiography (ECG), electroencephalography (EEG), and electromyography (EMG). Electrodiagnostic medicine (also EDX) is a medical subspecialty of neurology, clinical neurophysiology, cardiology, and physical medicine and rehabilitation. Electrodiagnostic physicians apply electrophysiologic techniques, including needle electromyography and nerve conduction studies to diagnose, evaluate, and treat people with impairments of the neurologic, neuromuscular, and/or muscular systems. The provision of a quality electrodiagnostic medical evaluation requires extensive scientific knowledge that includes anatomy and physiology of the peripheral nerves and muscles, the physics and biology of the electrical signals generated by muscle and nerve, the instrumentation used to process these signals, and techniques for clinical evaluation of diseases of the peripheral nerves and sensory pathways.
A cascaded anti-interference circuit suitable for an electrocardio monitoring device
ActiveCN120492801Bimprove signal-to-noise ratioSuppress power frequency interferenceMedical automated diagnosisFrequency selective two-port networksEcg signalNoise reduction
The embodiment of the application provides a cascade anti-interference circuit suitable for an electrocardio monitoring device, which comprises a sliding mean filter module, a notch filter module, a lifting wavelet decomposition module, a threshold calculation module, a threshold processing module and a lifting wavelet reconstruction module; while ensuring a small circuit scale, the ECG signal collected by the wearable electrocardio monitoring device is subjected to hierarchical noise reduction processing, and high signal-to-noise ratio ECG signal output is realized; for the ECG signal collected by the wearable electrocardio monitoring device, first, sliding mean filtering is performed to filter out baseline drift noise caused by human respiratory movement and other activities; the power frequency interference caused by the wired and wireless connection of the electromagnetic environment around the device is suppressed by using the notch filter module; finally, wavelet noise reduction is realized by using the lifting wavelet decomposition and reconstruction and threshold noise reduction method, the electromyographic interference caused by the autonomous or unconscious movement of the wearer is suppressed, and high signal-to-noise ratio ECG signal is output, which is used for physiological parameter extraction and electrocardio diagnosis.
Owner:WUHAN KANGNUOXIN SEMICON CO LTD
An alzheimer's disease electroencephalogram diagnosis method based on coherence brain network and scale characteristics, electronic device and computer system
This invention relates to an EEG diagnostic method, electronic device, and computer system for Alzheimer's disease based on coherent brain networks and scale features. The method includes the following steps: S1: Acquire EEG data and extract features to calculate their coherence, using these as sample points to construct a connectivity matrix; S2: Add scale features to the GCN algorithm; use an MLP classifier combined with SHAP values to sort the scale features, selecting strongly correlated scale features as nodes to fill the graph structure; S3: Set thresholds based on the coherent brain network, visualize the brain functional connectivity map, and locate specific brain regions; S4: Classify based on the coherent connectivity matrix and strongly correlated scale features as feature vectors and labels; S5: Output the classification results. The advantages are: effectively using EEG signals as an auxiliary diagnostic tool for early diagnosis of Alzheimer's disease; overcoming the difficulties in diagnosing Alzheimer's disease in existing technologies, and possessing high value for widespread application.
Owner:SHANGHAI UNIV OF MEDICINE & HEALTH SCI +1
Channel attention based anti-photoelectric isolation noise electrocardio monitoring system and method
The application discloses an anti-opto-isolated noise electrocardiogram monitoring system and method based on channel attention. The system comprises an opto-isolated acquisition module, a data preprocessing module, a data enhancement module, an intelligent processing module and a diagnosis output module. The method is as follows: first, an intelligent processing module is constructed, including a fractional Fourier transform layer and a one-dimensional convolutional neural network, and the one-dimensional convolutional neural network is trained using a data set; then, multi-lead electrocardiogram signals are collected through the opto-isolated acquisition module, the collected signals are preprocessed through adaptive length alignment and standardization, and the collected signals are data enhanced using an opto-spreading noise model; then, the enhanced signals are input into the intelligent processing module for feature extraction; finally, based on the extracted features, an electrocardiogram diagnosis result is output. The application improves the accuracy and robustness of electrocardiogram diagnosis in a real noise environment while ensuring electrical safety, and improves the diagnosis efficiency of cardiovascular diseases.
Owner:NANJING UNIV OF SCI & TECH
Electrocardio diagnosis system and device, storage medium and program product
The invention provides an electrocardio diagnosis system and device, a storage medium and a program product. The system comprises an electrocardio data acquisition device; the diagnosis terminal equipment is used for realizing the following functions: an AI diagnosis module used for performing AI diagnosis on a to-be-diagnosed person according to the acquired electrocardiogram data and outputting an acquired quality analysis conclusion, a disease diagnosis conclusion and corresponding two-dimensional basic confidence; the dynamic confidence coefficient calculation module is used for determining the dynamic confidence coefficient matched with the current scene according to the output of the AI diagnosis module; and the flow control module is used for comparing the dynamic confidence coefficient with a confidence coefficient threshold value preset for the current scene by each processing flow, and triggering the corresponding processing flow when the dynamic confidence coefficient is greater than or equal to the confidence coefficient threshold value preset for the current scene. By means of the technical scheme, the dynamic confidence coefficient and the confidence coefficient threshold value based on clinical scene adaptation are achieved, the accuracy of the AI diagnosis result and the processing timeliness and efficiency are improved, and the false positive rate is reduced.
Owner:纳龙健康科技股份有限公司
Multimodal electrocardiogram diagnosis large model architecture and training method
This invention discloses a multimodal ECG diagnostic large-scale model architecture, belonging to the field of machine learning technology, to solve the problem of inaccurate text-image understanding of existing ECG data. The architecture includes: an ECG encoder, an image encoder, a feature alignment module, and a large-scale language model. The ECG encoder encodes the input ECG time-series signal into a first feature representation; the image encoder encodes the input ECG image into a second feature representation; and the feature alignment module connects the ECG encoder and the image encoder. This invention uses contrastive learning to enable the model to understand the relationship between signals and text, ensuring the medical accuracy of the generated content.
Owner:HANGZHOU MINGXIN MEDICAL ARTIFICIAL INTELLIGENCE TECHNOLOGY CO LTD