Nec Corporation

Yumi Arai

Reishi Kondo

Masao Higuchi

Mitsuru Noma

Yuzo Senda

Osamu Hoshuyama

A lung sound analysis system includes a storage means for storing therein the history of auscultation observations on lung sound data of each of auscultation positions of a subject who is a heart failure patient, a calculation means for calculating appearance frequency of abnormal sounds for each of the auscultation positions on the basis of the history, a determination means for determining the sequence of the auscultation positions at which lung sounds are to be auscultated from the subject, on the basis of the calculated appearance frequency, and an acquisition means for giving guidance on the auscultation positions of the subject to an operator according to the determined sequence, and acquiring time-series acoustic signals including lung sounds from a guided auscultation position. The system supports medical decision-making for healthcare professionals monitoring heart failure patients.

Lung sound analysis system

A lung sound analysis system includes an acquisition means for acquiring time-series acoustic signals including lung sounds of a subject who is a heart failure patient, a determination means for determining a pause phase of breathing of the subject, a dividing means for dividing the time-series acoustic signals into those in a period of the pause phase of breathing of the subject and those in a period other than the pause phase, according to a result of the determination, and a detection means for detecting abnormality in the lung sounds from the time-series acoustic signals in the period other than the pause phase after the division. The system supports medical decision making for healthcare professionals monitoring heart failure patients.

A lung sound analysis system includes: an acquisition unit that obtains time-series acoustic signals including lung sounds from a heart failure patient; a determination unit that identifies the pause phase of the patient's breathing; a dividing unit that separates the time-series signals into those during the pause phase and those during other phases, based on the determination; a calculation unit that computes an index value representing the quality of the signals outside the pause phase, using the intensity of signals from both phases after separation; and a warning unit that issues alerts based on the calculated index value. The system supports medical decision making for healthcare professionals monitoring heart failure patients.

A trajectory estimation device that includes an acquisition unit that acquires wave data based on a wave detected by a plurality of sensors; a generation unit that generates a spectrogram using the wave data; an extraction unit that extracts a Doppler shift from the spectrogram; a selection unit that selects, as a sensor pair, two of the sensors that satisfy a preset selection condition for the Doppler shift; and an estimation unit that estimates a trajectory of a wave source, which is a source generating the wave, based on a positional relationship between the sensors constituting the sensor pair and a relationship of the Doppler shifts between the two sensors constituting the sensor pair.

Trajectory estimation device, trajectory estimation system, trajectory estimation method, and program recording medium

A trajectory estimation device that includes a waveform generation unit that generates, by using signals based on waves detected by at least three sensors, peak waveforms consisting of time series data of peak frequencies of the signals, a parameter estimation unit that estimates, from the peak waveforms relating to the waves detected by the at least three sensors, a trajectory parameter relating to a trajectory of a moving body having a wave source of the waves, and a trajectory estimation unit that estimates, for all combinations of two of the peak waveforms selected from among combinations of at least three of the peak waveforms, a wave source direction candidate for each of the waves by using the trajectory parameter, and estimate, as a trajectory of the moving body, overlapping trajectory candidates from among trajectory candidates estimated based on the wave source direction candidates.

A wave-source-direction estimation device includes: a plurality of input units that acquires, as input signals, electrical signals based on waves detected by a plurality of sensors; a signal selection unit that selects a plurality of pairs that are each a combination of two input signals from among a plurality of the input signals; a relative delay time calculation unit that calculates, as relative delay times, arrival time differences of the waves at the sensors that are supply sources of the two input signals composing each of the pairs, for each wave source direction; and an integrated-estimated-direction-information calculation unit that generates per-frequency estimated direction information for each of the pairs using the input signals composing each of the pairs and the relative delay times of each of the pairs and generates integrated estimated direction information by assigning a weight to and integrating the estimated direction information on all the pairs.

Wave-source-direction estimation device, wave-source-direction estimation method, and program storage medium

A vibration source estimation device, method and computer-readable medium are provided. The vibration source estimation device comprises a processor configured to execute instructions to receive data samples of a plurality of vibration signals generated at a vibration position, the data samples being associated with a plurality of frames. The processor is also configured to execute instructions to calculate a cross correlation function for each frame based on the data samples, calculate a weight for each frame based on a signal-to-noise ratio calculated from the cross correlation function, multiply the cross correlation function for each frame by the weight, calculate a sum of weighted cross correlation functions for respective frames as a weighted-cross-correlation function, and estimate the vibration position based on the weighted-cross-correlation function.

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Tokyo, Japan

Yumi Arai