Each conductor of a plurality of insulated conductors of a wiring harness extends between, and electrically connects, a corresponding terminal of a first electrical connector to either a corresponding terminal of an electrical connector jack of a plurality of electrical jacks located along the wiring harness, or to a corresponding terminal of a corresponding auscultatory sound-or-vibration sensor of the plurality of auscultatory sound-or-vibration sensors. The plurality of insulated conductors are organized in a plurality of distinct branches, each distinct branch originating either from the first electrical connector or from another portion of the wiring harness, and the locations of the plurality of distinct branches, in cooperation with the plurality of electrical jacks, if present, are implicitly suggestive of a corresponding location of the corresponding auscultatory sound-or-vibration sensor on a thorax of a test subject.

A system comprises processing circuitry and memory comprising program instructions that, when executed by the processing circuitry, cause the processing circuitry to: apply a first set of rules to first patient parameter data for a first determination of whether sudden cardiac arrest of a patient is detected; determine that a one or more context criteria of the first determination are satisfied; and in response to satisfaction of the context criteria, apply a second set of rules to second patient parameter data for a second determination of whether sudden cardiac arrest of the patient is detected. At least the second set of rules comprises a machine learning model, and the second patient parameter data comprises at least one patient parameter that is not included in the first patient parameter data.

A system comprises processing circuitry and memory comprising program instructions that, when executed by the processing circuitry, cause the processing circuitry to: apply a first set of rules to first patient parameter data for a first determination of whether sudden cardiac arrest of a patient is detected; determine that a one or more context criteria of the first determination are satisfied; and in response to satisfaction of the context criteria, apply a second set of rules to second patient parameter data for a second determination of whether sudden cardiac arrest of the patient is detected. At least the second set of rules comprises a machine learning model, and the second patient parameter data comprises at least one patient parameter that is not included in the first patient parameter data.

An information processing device includes a first acquisition unit that acquires biological information on a user, a first judgment execution unit that executes a first judgment on whether a first discomfort condition is satisfied or not based on first discomfort condition information specifying the first discomfort condition and the biological information, a second acquisition unit that acquires a sound signal, an acoustic feature detection unit that detects an acoustic feature based on the sound signal, a second judgment execution unit that executes a second judgment on whether a second discomfort condition is satisfied or not based on second discomfort condition information specifying the second discomfort condition and the acoustic feature, and an output judgment unit that judges whether first masking sound should be outputted or not based on a result of the first judgment and a result of the second judgment.

A leadless implantable medical device (IMD) and method of using same are provided. The IMD comprises: a housing, a fixation element, electrodes configured to sense electrical cardiac activity (CA) signals over a period of time, an HS sensor configured to sense HS signals over the period of time, memory to store specific executable instructions, and one or more processors. The one or more processors and method: identify a characteristic of interest (COI) of a heartbeat from the CA signals, calculate a center of mass (COM) for at least one HS based on the HS signals to obtain a corresponding at least one HS COM, and calculate at least one of a therapy-related (TR) delay or a sensing-related (SR) blanking interval (BI) based on the at least one HS COM.

A system and method for monitoring heart function based on heart sounds (HS) is provided. The system includes electrodes configured to sense electrical cardiac activity (CA) signals over a period of time. An HS sensor is configured to sense HS signals over the period of time. The system includes memory to store specific executable instructions and includes one or more processors that, when executing the specific executable instructions, is configured to: identify a characteristic of interest (COI) of a heartbeat from the CA signals. The processors overlay a HS search window onto an HS segment of the HS signals based on the COI from the CA signals and calculate a center of mass (COM) for at least one of S1 or S2 HS based on the HS segment of the HS signals within the search window to obtain a corresponding at least one of S1 COM or S2 COM. The processors calculate at least one of an electromechanical activation time (EMAT) or a systolic interval (SI) based on the at least one of S1 COM or S2 COM and record the at least one of the EMAT or SI.

Provided is a health monitoring device that extracts, in particular, an apical beat component from a trunk acoustic pulse wave and thus is also usable in the medical field. The health monitoring device (1000) of the present invention analyzes a trunk acoustic pulse wave to estimate the health condition of a person using a correlation of an indicator relating to a left ventricular pressure waveform which indicates the behavior of the heart, with a vibration frequency of a frequency component stemming from an apical beat, a diastolic time interval in a cardiac cycle, or blood pressure. The former is input information and the latter is output information of the heart through which blood circulates, and thus comparing these two pieces of information makes it possible to know a health condition relating to the function of the heart more accurately than conventionally.

Provided is a health monitoring device that extracts, in particular, an apical beat component from a trunk acoustic pulse wave and thus is also usable in the medical field. The health monitoring device (1000) of the present invention analyzes a trunk acoustic pulse wave to estimate the health condition of a person using a correlation of an indicator relating to a left ventricular pressure waveform which indicates the behavior of the heart, with a vibration frequency of a frequency component stemming from an apical beat, a diastolic time interval in a cardiac cycle, or blood pressure. The former is input information and the latter is output information of the heart through which blood circulates, and thus comparing these two pieces of information makes it possible to know a health condition relating to the function of the heart more accurately than conventionally.

The problem to be solved is to provide a new method and system for appropriately detecting a joint state from an acoustic of a joint. The present method comprises the step of obtaining, using a bio-acoustic sensor, an acoustic signal emitted by a joint during a time period that at least includes a first motion period wherein the joint changes from a first state to a second state, a pause period of the joint and a second motion period wherein the joint changes from the second state to the first state, the step of converting the obtained acoustic signal into time trend data that at least shows a relationship between an acoustic signal intensity and time, and the step of setting, from the time trend data, a basic threshold value regarding the acoustic signal intensity to calculate first acoustic information based on the basic threshold value, characterized in that the first acoustic information is an indicator of the joint state.

Systems and methods are disclosed to determine a measure of patient weight using existing medical device sensors, comprising receiving acceleration information of a patient and, if a value of the acceleration information exceeds an activity threshold over a measurement window, detecting patient steps in the measurement window using the acceleration information, determining a patient step rate over the measurement window using the detected patient steps, determining a measure of patient step force for the measurement window, and determining a measure of patient weight using the determined patient step rate and measure of patient step force.