Zargis Medical Corporation

3m Innovative Properties Company

Siemens Corporate Research, Inc.

Raymond L Watrous

Nathaniel Reichek

Robert Steven Kody

Daniel J Rogers

Joseph P Keller

A stethoscope includes a flexible chest piece including a top surface, a bottom surface, a side surface defining a slope between the top surface and the bottom surface, and a groove extending along an inner circumference of the chest piece. A diaphragm is positioned within the groove.

Stethoscope

Methods and systems for mapping a physiological signal into clinical guideline parameters are disclosed. A physiological signal having a characteristic that may represent an anomaly is received and mapped to a clinical guideline condition space. Probabilities are determined that the mapped signal with which the anomaly may be associated represents a first clinical guideline condition corresponding to a referral indication or a second clinical guideline condition corresponding to an absence of the referral indication. The determined probability is presented and a referral decision is made responsive to the determined probability that the anomaly is associated with the first clinical guideline condition.

Categorizing automatically generated physiological data based on industry guidelines

Methods and systems for simulating a phonocardiogram (PCG) signal that includes an anomalous condition are provided. The method generates pressure and flow signals from a lumped-parameter heart model responsive to anomaly parameters. The anomaly parameters represent the anomalous condition. A timing profile or the timing profile and an amplitude profile are extracted from at least one of the generated pressure and flow signals. An anomalous signal is generated using the anomaly parameters and the extracted timing profile or timing profile and amplitude profile. The anomalous signal is time-aligned and combined with a predetermined non-anomalous signal to represent the PCG signal.

Automatic generation of heart sounds and murmurs using a lumped-parameter recirculating pressure-flow model for the left heart

A handheld auscultatory scanner continuously obtains a heart sound signal from a patient, using a noninvasive passive acoustic sensor, allowing free-form protocol, analysis and display of the heart sound signal on a handheld processing unit in a graphical manner such that a single heart cycle is displayed in a synchronized manner, along with summary results of the processing of the acoustic signal. The results are presented in terms of standard auscultatory findings. The combination of summary findings, heart sound display and audible signal provides a method for assisting in patient screening for heart conditions and for teaching auscultation techniques.

Handheld auscultatory scanner with synchronized display of heart sounds

A method for extracting and evaluating features from cardiac acoustic signals includes the steps of obtaining a cardiac acoustic signal, extracting physiologically significant features from the cardiac acoustic signal using a neural network, analyzing the cardiac acoustic signal with a wavelist decomposition to extract time-frequency information, and identifying basic heart sounds using neutral networks applied to the extracted time-frequency information. A method for determining a status of heart murmurs includes the steps of obtaining a cardiac acoustic signal, detecting a murmur, if any, from the cardiac acoustic signal, and determining whether the murmur is one of functional and pathological based upon expert rules.

Multi-modal cardiac diagnostic decision support system and method

An apparatus according to an embodiment of the present invention is provided for sensing acoustic signals. The apparatus includes a housing having an apertured posterior member, a sensing unit extending through the apertured posterior member for interfacing by contact with a patient, a cursor control for positioning a cursor on a display, the display located anteriorially on the housing, a button for fixing a position of the cursor in the display, indicating the position on the housing with respect to the patient, and a circuit for causing the cursor to move about the display in response to the cursor control, and for transmitting the acoustic signal.

Handheld sensor for acoustic data acquisition

A diagnostic decision support system provides diagnostic decision support for auditory evaluation of anatomical features and is applicable to virtually any living creature. The system processes an acoustic signal for medical applications by acquiring acoustic data representative of an acoustic signal associated with an anatomical function. The acquired acoustic data is stored in a file associated with a patient medical record. The acquired acoustic data and medical record information is automatically analyzed to determine physiologically significant features useful in medical diagnosis. Information is generated supporting medical diagnosis based on the automatic analysis. In addition, the analysis of the acquired acoustic data may be partially automatic and involve User input of information for use in facilitating diagnostic decision making. The system also processes patient identification information and acoustic test type information (e.g. identifying anatomical function, anatomical condition, auditory test anatomical location or patient posture).

System for processing audio, video and other data for medical diagnosis and other applications

A method for extracting features from cardiac acoustic signals includes the steps of obtaining a cardiac acoustic signal, and extracting physiologically significant features from the cardiac acoustic signal using a neural network. A method for evaluating cardiac acoustic signals includes the steps of obtaining a cardiac acoustic signal, analyzing the cardiac acoustic signal with a wavelet decomposition to extract time-frequency information, and identifying basic heart sounds using neural networks applied to the extracted time-frequency information. A method for determining cardiac event sequences from cardiac acoustic signals includes the steps of obtaining a cardiac acoustic signal, and processing a sequence of features extracted from the cardiac acoustic signal by a probabilistic finite-state automaton to determine a most probable sequence of cardiac events given the cardiac acoustic signal. A method for extracting findings from cardiac acoustic signals includes the steps of obtaining a cardiac acoustic signal, processing the cardiac acoustic signal to determine a most probable sequence of cardiac events given the cardiac acoustic signal, and extracting the clinical findings from the sequence of cardiac events. A method for determining a status of heart murmurs includes the steps of obtaining a cardiac acoustic signal, detecting a murmur, if any, from the cardiac acoustic signal, and determining whether the murmur is one of functional and pathological based upon expert rules.

According to an embodiment of the present invention, an acoustic signal sensing apparatus is provided. The apparatus includes a housing having an apertured posterior and three studs wherein at least one stud is an electrode providing a temporal reference signal and a sensing unit contacting a patient and capturing an acoustic cardiovascular signal and the temporal reference signal, wherein a portion of the sensing unit is located within the housing. The apparatus further includes a telemetry sensor connected to the sensing unit, communicating the acoustic signal, the temporal reference signal, and a position of the sensing unit with respect to the patient to a data processor.

Delays in event detection in time-varying data can be reduced by predicting the time-varying data and then detecting the event in the predicted data. This finds application in the triggering of medical imaging devices, where physiological events can be detected in the time-varying data. An artificial neural network can be trained to predict data such as ECG signals from which a detection algorithm can accurately predict the occurrence of an event that will serve as a reference point for triggering.

Time series prediction for event triggering

A large universe of signals can be rapidly examined for a specific phenomenon by searching for a morphology representative of a subgroup of the universe. A detector for each such representative morphology is arranged in parallel to examine a signal input and provide an indication of positive identification a specific morphology.

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Belle Mead, NJ, United States of America

Raymond L Watrous