The present disclosure pertains to systems and methods for encoding and/or decoding brain activity signals for data reduction. In a non-limiting embodiment, first user data associated with a first sleep session of a user is received. The first user data is determined to include at least a first instance of a first sleep feature being of a first data size. A first value representing the first instance during a first temporal interval is determined. First encoding data representing the first value is determine, the first encoding data being of a second data size that is less than the first data size. Second user data is generated by encoding the first user data using the first encoding data to represent the first instance in the second user data, and the second user data is stored.

A heart-rate associated with a heartbeat signal is determined. A transform is selected based on the determined heart-rate associated with the heartbeat signal and a reference heart-rate associated with a dictionary of a sparse approximation model. The transform is selected independent of other factors associated with generation of the heartbeat signal. The selected transform is applied to the dictionary of the sparse approximation model, generating an adjusted dictionary of the sparse approximation model. Anomalous heartbeats in the heartbeat signal are detected using the adjusted dictionary of the sparse approximation model.

Systems, devices and methods transmit data from a patient device to a location, for example a remote location, where the patient is monitored. The system may comprise a server system, for example a backend server system, a gateway and the patient worn device. The gateway can be configured to communicate with the patient worn device in response to a list transmitted from the server, for example an approved patient device list transmitted from the server to the gateway. The gateway may exclude communication with patient worn devices that are not on the list. This use of the list can control data throughput from the patient device to the gateway and also from the gateway to the server, such that the communication from the device on the list to the server is maintained and appropriate information can be reliably sent from the patient device to the server.

A heartrate monitor detects heartbeats in a test signal. A local heartrate and an energy of acceleration are associated with the detected heartbeats. Detected heartbeats are included or excluded from a test set of heartbeats based on the local heartrate and energy of acceleration associated with the respective heartbeats. Anomalous heartbeats in the test set of heartbeats are detected using a sparse approximation model. The heartrate monitor may detect heartbeats in a training heartbeat signal. A reference heart rate and an energy of acceleration are associated with detected beats of the training heartbeat signal and selectively included in a set of training data based on the heart rate and energy of acceleration associated with the detected beat in the training heartbeat signal. A dictionary of the sparse representation model may be generated using the set of training data.

Techniques are provided for generating and processing compressed sensor data. Sensor signals can be collected using one or more sensors. The sensor signals can be compressed using a compression data structure. In some instances, the compressed signal corresponds to a sampling rate at or below the Nyquist sampling rate. The compressed signal can be compared to one or more templates. Events within the compressed signal can be detected and characterized based on the comparison.

Disclosed herein are methods and systems for receiving an encoded data packet, one or more activation commands, and a communication identifier, decoding the received data packet, validating the decoded received data packet, and executing one or more routines associated with the respective one or more activation commands.

Methods and systems are described for sensing and recovery of a biological signal using generative-model-based compressive sensing. A transformation is applied to sparsify the quasi-periodic signal removing morphology parameters and leaving temporal parameters. The sparsified signal is sampled and the sampled signal data is transmitted to a base station. A homotopy recovery algorithm is applied to the received sampled signal data by the base station to recover the temporal parameters of the biological signal. Generative modelling is applied using previously captured morphology parameters to generate a reconstructed signal. Finally, the reconstructed signal is adjusted and scaled based on the recovered temporal parameters to provide a reconstructed signal that is diagnostically equivalent to the original biological signal.

Electrocardiogram (ECG) data is compressible at high compression ratios using suitable compressive sensing techniques. Methods of detecting QRS complexes in an ECG signal may comprise receiving compressively-sensed measurements of an ECG signal; constructing an estimate of the ECG signal from the received compressively-sensed measurements, and detecting QRS complexes in the estimate of the ECG signal. QRS complexes may be detected by computing the first-order difference of the estimate of the ECG signal and processing the first-order difference of the estimate of the ECG signal to locate one or more significant natural blocks, each indicating a QRS complex in the ECG signal. QRS complexes may also be detected by using a conventional QRS detection algorithm on the estimate of the ECG signal. Also disclosed are related systems for detecting QRS complexes and for compressively sensing ECG signals.

Physiological monitoring can be provided through a lightweight wearable monitor that includes two components, a flexible extended wear electrode patch and a reusable monitor recorder that removably snaps into a receptacle on the electrode patch. The wearable monitor sits centrally (in the midline) on the patient's chest along the sternum oriented top-to-bottom. The placement of the wearable monitor in a location at the sternal midline, with its unique narrow hourglass-like shape, significantly improves the ability of the wearable monitor to cutaneously sense cardiac electrical potential signals, particularly the P-wave and, to a lesser extent, the QRS interval signals indicating ventricular activity in the ECG waveforms. Additionally, the monitor recorder includes an ECG sensing circuit that measures raw cutaneous electrical signals and performs signal processing prior to outputting the processed signals for sampling and storage.

An ECG signal lossless compression system includes: a signal difference value generating module and a compression module. The signal difference value generating module performs an adaptive linear prediction encoding on an ECG signal, so as to generate a plurality of signal difference values corresponding to each datum of the ECG signal;he compression module divides the signal difference values into a plurality of groups and performs an adaptive linear lossless compression encoding on each group, so as to generate a plurality of window compression streams, wherein each group corresponds to a bit reference index configured to be a compression encoding parameter of the adaptive linear lossless compression encoding.