A dynamic bionic heart phantom is used for an MRI system, a control method and a testing method. The dynamic bionic heart phantom includes a bionic heart phantom, a control system, positive pressure devices and a negative pressure device; the bionic heart phantom includes a water tank and a heart phantom arranged in the water tank, and the heart phantom is connected to the control system through four air pipes; the control system includes an antimagnetic control device and a control PC, and the antimagnetic control device is composed of a measurement and control module, four proportional flow values, a power module and a magnetic shielding box; the positive pressure devices, including gas, gas cylinders and pressure reducing valves, are connected to two gas inlet interfaces of the control system respectively; and the negative pressure device includes a vacuum pump and a negative pressure container.

Systems are provided for generating data representing electromagnetic states of a heart for medical, scientific, research, and/or engineering purposes. The systems generate the data based on source configurations such as dimensions of, and scar or fibrosis or pro-arrhythmic substrate location within, a heart and a computational model of the electromagnetic output of the heart. The systems may dynamically generate the source configurations to provide representative source configurations that may be found in a population. For each source configuration of the electromagnetic source, the systems run a simulation of the functioning of the heart to generate modeled electromagnetic output (e.g., an electromagnetic mesh for each simulation step with a voltage at each point of the electromagnetic mesh) for that source configuration. The systems may generate a cardiogram for each source configuration from the modeled electromagnetic output of that source configuration for use in predicting the source location of an arrhythmia.

Systems are provided for generating data representing electromagnetic states of a heart for medical, scientific, research, and/or engineering purposes. The systems generate the data based on source configurations such as dimensions of, and scar or fibrosis or pro-arrhythmic substrate location within, a heart and a computational model of the electromagnetic output of the heart. The systems may dynamically generate the source configurations to provide representative source configurations that may be found in a population. For each source configuration of the electromagnetic source, the systems run a simulation of the functioning of the heart to generate modeled electromagnetic output (e.g., an electromagnetic mesh for each simulation step with a voltage at each point of the electromagnetic mesh) for that source configuration. The systems may generate a cardiogram for each source configuration from the modeled electromagnetic output of that source configuration for use in predicting the source location of an arrhythmia.

An insertable cardiac monitor (ICM) with induction-based recharging capabilities and a transmitting coil for recharging the same are disclosed. The length of the monitoring performed by the ICM is extended and the functionality of the ICM enhanced, by including an internal energy harvesting module that allows for charging the ICM at a high speed without burning the patient or overheating components of the ICM. Internally, the energy harvesting module includes at least two overlapping receiving coils that are spaced to be orthogonal to each other and that have a tilt angle of substantially 45°. Such overlapping wire combination allows to minimize mutual inductance of the solenoid coils and increase the rate at which energy can be provided to the energy harvesting module. Further, the rate at which the energy is transmitted from the outside can be increased by defining in a transmitting coil a substantially triangular gap.

An insertable cardiac monitor (ICM) with induction-based recharging capabilities and a transmitting coil for recharging the same are disclosed. The length of the monitoring performed by the ICM is extended and the functionality of the ICM enhanced, by including an internal energy harvesting module that allows for charging the ICM at a high speed without burning the patient or overheating components of the ICM. Internally, the energy harvesting module includes at least two overlapping receiving coils that are spaced to be orthogonal to each other and that have a tilt angle of substantially 45°. Such overlapping wire combination allows to minimize mutual inductance of the solenoid coils and increase the rate at which energy can be provided to the energy harvesting module. Further, the rate at which the energy is transmitted from the outside can be increased by defining in a transmitting coil a substantially triangular gap.

A method of heart failure prediction comprising obtaining a raw electrocardiogram (ECG) signal by a sensor, generating a clean ECG signal according to the raw ECG signal by a pre-processing circuit, performing, by a feature extraction circuit, a principal component decomposition and a heart rate feature analysis according to the clean ECG signal to generate a feature vector with a plurality of features, and generating a prediction to indicate whether the heart failure will happen in a specified period according to the feature vector by a predicting model circuit.

A method of heart failure prediction comprising obtaining a raw electrocardiogram (ECG) signal by a sensor, generating a clean ECG signal according to the raw ECG signal by a pre-processing circuit, performing, by a feature extraction circuit, a principal component decomposition and a heart rate feature analysis according to the clean ECG signal to generate a feature vector with a plurality of features, and generating a prediction to indicate whether the heart failure will happen in a specified period according to the feature vector by a predicting model circuit.

An apparatus yields signals that are equivalent to ECG signals and allow determination of a heartbeat interval or heart rate from bio-vibration signals including vibrations derived from heartbeats. An ECG meter acquires ECG signals of a sample, and a piezoelectric sensor acquires bio-vibration signals of the sample simultaneously. The bio-vibration signals include beating vibration signals derived from heartbeats. A learning unit of a prediction modeling apparatus establishes a prediction model by machine learning in which ECG signals are used as teaching data, and model input signals obtained by performing a specified processing on the bio-vibration signals are input. The learning unit delivers the prediction model to a prediction unit of a signal processing apparatus. The prediction model predicts and outputs pECG signals upon input of model input signals obtained by performing a specified processing on bio-vibration signals acquired from a subject under prediction with a piezoelectric sensor.

An electromechanical system for generating a CPR-corrupted ECG signal is provided. The electromechanical system may include an ECG signal generator electrically coupled to a first contact of an AED. The electromechanical system may further include a potentiometer electrically coupled to the ECG signal generator and a second contact of the AED. The electromechanical system may further include a compression mechanism. The compression mechanism may be configured to receive a vertical force and adjust an impedance of the potentiometer according to the vertical force. The compression mechanism may include a rack having a plurality of teeth and an initial position. The rack may be configured to translate to a second position according to the vertical force. The compression mechanism may further include a gear with a plurality of teeth engaged with the teeth of the rack such that the gear rotates according to the translation of the rack.

An electromechanical system for generating a CPR-corrupted ECG signal is provided. The electromechanical system may include an ECG signal generator electrically coupled to a first contact of an AED. The electromechanical system may further include a potentiometer electrically coupled to the ECG signal generator and a second contact of the AED. The electromechanical system may further include a compression mechanism. The compression mechanism may be configured to receive a vertical force and adjust an impedance of the potentiometer according to the vertical force. The compression mechanism may include a rack having a plurality of teeth and an initial position. The rack may be configured to translate to a second position according to the vertical force. The compression mechanism may further include a gear with a plurality of teeth engaged with the teeth of the rack such that the gear rotates according to the translation of the rack.