Systems, apparatuses and methods may provide for technology that converts a plurality of multi-channel time-synchronized signals into a plurality of image patches, combines the plurality of image patches into an image, and generates, by a transformer neural network, a classification result based on the image.

Systems, apparatuses and methods may provide for technology that converts a plurality of multi-channel time-synchronized signals into a plurality of image patches, combines the plurality of image patches into an image, and generates, by a transformer neural network, a classification result based on the image.

The present application discloses an amplifier circuit, a chip and an electronic device, which generates a positive output signal and a negative output signal according to a positive input signal and a negative input signal, wherein the positive input signal and the negative input signal have a corresponding input differential-mode voltage and input common-mode voltage, and the positive output signal and the negative output signal have a corresponding output differential-mode voltage and output common-mode voltage, and the amplifier circuit includes: an amplifying unit, configured to receive the positive input signal and the negative input signal and generate the positive output signal and the negative output signal; and an attenuation unit, including: a positive common-mode capacitor and a negative common-mode capacitor, configured to attenuate the input common-mode voltage below a first specific frequency.

A medical device is configured to sense first ventricular event signals from a first cardiac electrical signal and sense second ventricular event signals from a second cardiac electrical signal. The medical device is configured to determine sensed event data in response to the first ventricular event signals and the second ventricular event signals. The medical device may select one of the first cardiac electrical signal or the second cardiac electrical signal for providing input for tachyarrhythmia detection based on the sensed event data.

A computing device, such as a wearable device, may include at least two electrodes mounted on a body. The computing device may determine an electrical signal associated with a circuit that includes the at least two electrodes and the user. A pressure applied to at least one electrode of the at least two electrodes may be determined from the electrical signal, and at least one function of the computing device may be implemented, based on the pressure.

An electronic device includes a housing including a first cover member, a second cover member, and a side member enclosing a space between the first cover member and the second cover member; a support member coupled to or formed integrally with the side member; a printed circuit board disposed in the space and including a biometric circuit; a first conductive portion disposed at least partially in the side member; a second conductive portion and third conductive portion disposed at least partially in the second cover member and electrically connected to the printed circuit board; and at least one conductive path disposed in the space, configured to electrically connect the biometric circuit and the first conductive portion, and formed on the support member. The biometric circuit receives a biometric signal based on the first conductive portion, the second conductive portion, the third conductive portion, and the at least one conductive path.

In the present invention, a right leg drive RLD monitoring system is employed on a medical computing system/computer, such as an ECG, HEMO and/or EP monitoring, mapping and/or recording system, that includes a number of RLD circuits to be utilized for different procedures or monitoring states to be performed using the system. The RLD monitoring system operates to actively monitor and/or record the feedback voltage to the RLD isolated from the patient. Using the measured feedback voltage data, the RLD monitoring system can identify and determine if the RLD circuit in use is approaching saturation, has reached saturation and the duration the RLD circuit was in saturation. The RLD monitoring system can concurrently and/or subsequently select and/or provide selection information regarding an optimal RLD circuit to be utilized to most effectively perform the desired function of the RLD in the procedure being performed using the monitoring, mapping and/or recording system.

In the present invention, a right leg drive RLD monitoring system is employed on a medical computing system/computer, such as an ECG, HEMO and/or EP monitoring, mapping and/or recording system, that includes a number of RLD circuits to be utilized for different procedures or monitoring states to be performed using the system. The RLD monitoring system operates to actively monitor and/or record the feedback voltage to the RLD isolated from the patient. Using the measured feedback voltage data, the RLD monitoring system can identify and determine if the RLD circuit in use is approaching saturation, has reached saturation and the duration the RLD circuit was in saturation. The RLD monitoring system can concurrently and/or subsequently select and/or provide selection information regarding an optimal RLD circuit to be utilized to most effectively perform the desired function of the RLD in the procedure being performed using the monitoring, mapping and/or recording system.

To provide an electrocardiogram analysis system capable of determining the need for an electric shock to a patient undergoing cardiopulmonary resuscitation (CPR) with a higher accuracy. An electrocardiogram analysis system includes electrocardiogram (ECG) signal acquiring means 11, ECG signal sampling means 12, ECG spectrogram transforming means 13, impedance signal acquiring means 21, impedance signal sampling means 22, impedance spectrogram transforming means 23, a convolutional neural network (CNN) 4 including an input layer 4I, an output layer 4O, sample data accumulation means 4L, and sample data input means 4T, and electric shock indication reporting means 5. The CNN is a priori provided with sample data including sample ECG spectrograms and sample impedance spectrograms obtained from a large number of subjects, and sample response data on the need for an electric shock, and is optimized by self-learning the sample data.

To provide an electrocardiogram analysis system capable of determining the need for an electric shock to a patient undergoing cardiopulmonary resuscitation (CPR) with a higher accuracy. An electrocardiogram analysis system includes electrocardiogram (ECG) signal acquiring means 11, ECG signal sampling means 12, ECG spectrogram transforming means 13, impedance signal acquiring means 21, impedance signal sampling means 22, impedance spectrogram transforming means 23, a convolutional neural network (CNN) 4 including an input layer 4I, an output layer 4O, sample data accumulation means 4L, and sample data input means 4T, and electric shock indication reporting means 5. The CNN is a priori provided with sample data including sample ECG spectrograms and sample impedance spectrograms obtained from a large number of subjects, and sample response data on the need for an electric shock, and is optimized by self-learning the sample data.