An ECG monitoring device includes a vibration meter sensor unit including at least one vibration meter sensor attached to an instrument at which a person to be observed is positioned, and configured to acquire a vibration signal by detecting a vibration transmitted through the instrument in a non-contact or non-invasive method, a filter unit configured to extract a seismocardiography signal (“SCG signal”) generated by a heart vibration of the person to be observed by receiving the vibration signal and filtering a predetermined frequency band from the received vibration signal, and an ECG waveform acquisition unit including an artificial neural network learned in advance and configured to generate an electrocardiogram signal (“ECG signal”) corresponding to the applied SCG signal according to a learned method.

A safety circuit, in the form of a switch box, for coupling with a catheter, detects DC leakage or emission from an amplifier circuit of the catheter, and switches a switch to immediately terminates (cuts-off) power to the amplifier circuit. This immediate power termination instantaneously stops DC leakage, which if left unchecked or otherwise undetected, may reach the heart, and disrupt its electrical activity and cause other damage.

Systems, methods, and computer program product embodiments are disclosed for removing any fixed frequency interfering signal from an input signal without introducing artifacts that are not part of the original signal of interest. An embodiment operates by using a virtual buffer with a length that matches a length of one cycle of an interfering signal. The embodiment extracts the interfering signal into the virtual buffer. For a sample in the next cycle of the interfering signal that corresponds to a virtual memory location for the virtual buffer, the embodiment can update one or more physical memory locations of the virtual buffer that are in the vicinity of the virtual memory location. This use of virtual buffer can remove any interfering signal without creating the artifacts associated with conventional notch filters.

A measurement station includes an electrocardiogram acquisition system, two control electrodes configured to contact a user, and an electrical connection circuit, the electrical connection circuit comprising a feedback loop connected to the two control electrodes.

Switching systems are positioned along a bidirectional signal carrying line, typically between an electrode in a catheter at the heart of a patient, and an external console. The switching system provides for switching the bidirectional signal carrying line between: a main line, which carries acquired electrocardiac signals from the electrode of the catheter at the heart of the patent to the external console, via a switch unit; and, a bypass line, which carries pacing signals, directly from the external console to the electrode of the catheter. The bypass line provides an uninterrupted electrical connection between the electrode and the external console, thus avoiding the switch unit.

A signal measurement circuit comprises: a sensor electrode layer connected via a sensor cable to a measurement amplifier circuit; an active shielding layer, which runs along a side of the sensor electrode layer that faces away from the patient; and a first insulating layer that runs between the sensor electrode layer and the active shielding layer. The sensor electrode layer and the active shielding layer are embodied to be electrically conductive.

An object of the present invention is to provide a new electric device for defibrillation and a method for generating a defibrillation signal. The electric device for defibrillation includes an electrocardiogram waveform input unit; and an enable signal generating unit, wherein the electric device for defibrillation is configured to generate an enable signal from the enable signal generating unit after a peak of an event is surpassed and when or after condition 1 is satisfied, the event being estimated to be an R-wave of an electrocardiogram waveform, the electrocardiogram waveform being obtained from a human body and inputted from the electrocardiogram waveform input unit, and the condition 1 is that a differential value in a differentiated waveform generated based on the electrocardiogram waveform, which corresponds to the event estimated to be the R-wave, is a negative constant C.sub.3 value or less.

A wearable stethoscope includes a sound sensing device for collecting heart sound signals of the body, an electrocardiogram sensing device for collecting electrocardiogram signals of the body, a processing unit, powered by a power source, coupled to the sound sensing device and the electrocardiogram sensing device to perform data preprocessing on the above-mentioned signals to remove background noise. An external electronic computing device is set up to analyze and process the fed pre-processed ECG signal and heart sound signal, perform feature extraction in combination with the user's physiological parameters and medical records to obtain related feature vectors, input the feature vectors into a screening model, obtain an evaluation value and give corresponding suggestions. After screening, users can upload the verification results to the cloud database to expand the existing training samples for further optimizing the parameters of the screening model.

A wearable stethoscope includes a sound sensing device for collecting heart sound signals of the body, an electrocardiogram sensing device for collecting electrocardiogram signals of the body, a processing unit, powered by a power source, coupled to the sound sensing device and the electrocardiogram sensing device to perform data preprocessing on the above-mentioned signals to remove background noise. An external electronic computing device is set up to analyze and process the fed pre-processed ECG signal and heart sound signal, perform feature extraction in combination with the user's physiological parameters and medical records to obtain related feature vectors, input the feature vectors into a screening model, obtain an evaluation value and give corresponding suggestions. After screening, users can upload the verification results to the cloud database to expand the existing training samples for further optimizing the parameters of the screening model.

The present invention relates to a system and method of emotion recognition. An emotion recognition system may utilize a Valence-Arousal factor along with training data. The training data may exist as emotions assigned to actual measurements of user inputs. The actual measurements of user inputs may be assigned to a plurality of points on the Valence-Arousal model. A user input acquisition device may be used to collect actual measurements of user inputs. A processor may utilize an algorithm to assign user emotions based on the training data. A user may provide feedback on the assigned user emotions, and the training data may be updated based on the user feedback, depending on whether the user feedback is considered an outlier to the training data.