Systems and methods are provided for monitoring progression of a cardiac disease in a patient by providing cardio-vibrational image matrixes and/or ECG image matrices generated using sensor data supplied by a medical device. In some examples, cardio-vibrational image matrices and/or ECG image matrices are output as image files. In some implementations, systems and methods are provided for using such cardio-vibrational image matrices and/or an ECG image matrices, and/or other clinical information, using machine learning classifiers, to assess cardiac risk in a patient. In some implementations, systems and methods are provided for using cardio-vibrational image matrixes and/or ECG image matrices, and/or other clinical information for real-time analysis of cardiac risk.

Active auscultation may be used to determine organ (e.g., lung or heart) characteristics of users. An acoustic or piezo-electric signal (e.g., a pulse, a tone, and/or a broadband pulse) may be projected into an animal (typically human) body or thorax. The signal interacts with the body, or lungs, and in some cases may induce resonance within the body/lungs. A resultant signal may be emitted from the body which may be analyzed to determine, for example, a lung's resonant frequency or frequencies and/or how the sound is otherwise absorbed, reflected, or modified by the body. This information may be indicative of lung characteristics such as lung capacity, a volume of air trapped in the lungs, and/or the presence of COPD.

Active auscultation may be used to determine organ (e.g., lung or heart) characteristics of users. An acoustic or piezo-electric signal (e.g., a pulse, a tone, and/or a broadband pulse) may be projected into an animal (typically human) body or thorax. The signal interacts with the body, or lungs, and in some cases may induce resonance within the body/lungs. A resultant signal may be emitted from the body which may be analyzed to determine, for example, a lung's resonant frequency or frequencies and/or how the sound is otherwise absorbed, reflected, or modified by the body. This information may be indicative of lung characteristics such as lung capacity, a volume of air trapped in the lungs, and/or the presence of COPD.

Systems and methods for correlating inputs found external to a user to inputs measured from the user are disclosed, using wearables, various types of non-wearable sensors, and other external data sources and mobile device technology. Pattern matching and rules can be used to provide useful suggestions or control external machines based on the correlated inputs.

Systems and methods for correlating inputs found external to a user to inputs measured from the user are disclosed, using wearables, various types of non-wearable sensors, and other external data sources and mobile device technology. Pattern matching and rules can be used to provide useful suggestions or control external machines based on the correlated inputs.

Introduced here are computer programs and associated computer-implemented techniques for deriving insights into the health of patients through analysis of audio data generated by electronic stethoscope systems. A diagnostic platform may be responsible for examining the audio data generated by an electronic stethoscope system so as to gain insights into the health of a patient. The diagnostic platform may employ heuristics, algorithms, or models that rely on machine learning or artificial intelligence to perform auscultation in a manner that significantly outperforms traditional approaches that rely on visual analysis by a healthcare professional.

A method to evaluate respiratory health includes obtaining (i) one or more lung acoustic signals and (ii) one or more bioimpedance spectroscopy signals, wherein the one or more acoustic signals and the one or more bioimpedance spectroscopy signals are concurrently acquired over multiple respiratory cycles; generating values for (i) a first set of plurality of statistical features and/or a first set of time-frequency domain features using the obtained one or more lung acoustic signals and (ii) a second set of plurality of statistical features and/or a second set of time-frequency domain features using the obtained one or more bioimpedance spectroscopy signals; and generating, using one or more trained classifiers, a respiratory health value representative of a respiratory health of the patient by application of the values of the first and second sets of plurality of statistical features and time-frequency domain features to the one or more trained classifiers.

A method and electronic device for processing a waveform are disclosed. The waveform is representative of bodily sounds. The method includes acquiring the waveform from the sound recording component and having a low-frequency component and a high-frequency component, selecting a target moving averaging filter amongst a first moving averaging filter and a second moving averaging filter for filtering the waveform. The first moving averaging filter is to be used for preserving the low-frequency component of the waveform, and the second moving averaging filter is to be used for preserving the high-frequency component of the waveform. The method includes applying the target moving averaging filter on the waveform for reducing noise in the waveform, thereby generating a second waveform.

Predicting gastrointestinal impairment may involve obtaining intestinal sounds of a patient to generate audio data, identifying predefined spectral events in the audio data that are predictive of subsequent gastrointestinal impairment, the spectral events being defined by predefined parameters, and predicting the likelihood of subsequent gastrointestinal impairment relative to the identified spectral events.

A system and method for allowing any surgeon, including those surgeons who perform a fewer number of a replacement procedure as compared to a more experienced surgeon who performs a greater number of procedures, to provide an improved likelihood of a favorable outcome approaching, if not exceeding, a likelihood of a favorable outcome as performed by a very experienced surgeon with the replacement procedure. Force sensing is included to aid in quantifying installation of an implant, particularly a cup into a pelvic bone.