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JP-7856635-B2 - Implantable medical devices, computer program products, and methods for classifying the heart condition of a living human or animal.

JP7856635B2JP 7856635 B2JP7856635 B2JP 7856635B2JP-7856635-B2

Inventors

  • ヴァイス、インゴ

Assignees

  • バイオトロニック エスエー アンド カンパニー カーゲー

Dates

Publication Date
20260511
Application Date
20211005
Priority Date
20201008

Claims (13)

  1. An implantable medical device for detecting cardiac signals from a living human or animal and stimulating the human or animal heart, comprising a processor (630), a memory unit (640), and a detection unit (620) for detecting cardiac signals from a living human or animal, The storage unit (640) has a computer-readable program that, when executed on the processor (630), causes the processor (630) to perform the following steps, the steps being: a) Detect the heart signals (200, 610) of a living human or animal in a time-dependent manner. b) Define a signal segment (201) that extends over an adjustable first time length of the cardiac signal (200, 610), c) The signal segment (201) is subdivided into a first block (210) in at least a first section of the signal segment, d) Determine the total number of the first block (210), e) Determine the signal amplitude scale (220, 320, 420) in each of the first blocks (210), and characterize the signal amplitude as the difference between the minimum signal deflection and the maximum signal deflection within the corresponding block. f) Determine the number of first blocks (210) in which the scale (220, 320, 420) of the signal amplitude is less than a first threshold (330, 430) that can be determined in advance. g) Calculate a first quotient (q1) from the number of first blocks (210) whose scales (220, 320, 420) of the signal amplitude are less than the first threshold (330, 430), and the total number of first blocks (210). h) Compare the first quotient (q1) with the second threshold (350, 450), i) A device comprising the step of classifying the cardiac state of a human or animal organism into a first class if the first quotient (q1) is greater than the second threshold (350, 450), or classifying the cardiac state of a human or animal organism into a second class if the first quotient (q1) is not greater than the second threshold (350, 450), wherein one of the first and second classes represents the physiological state of the organism, the other of the first and second classes represents the pathophysiological state of the organism, the physiological state is sinus rhythm or non-tachycardic state, and the pathophysiological state is tachycardic state.
  2. The computer-readable program causes the processor (630) to subdivide the heart signal (200, 610) into blocks (210, 212) in a plurality of sections, the widths (211, 213) of the blocks (210, 212) differ from each other in at least two of the plurality of sections, the total number of blocks (210, 212) is determined in each section, the scale of the signal amplitude (220, 320, 420) in each of the blocks (210, 212) is determined, the number of blocks (210, 212) in each section whose scale of the signal amplitude (220, 320, 420) is less than a predetermined threshold (330, 430) for each section is determined, and for each section, the quotient (q1, q2) is the number of blocks in each section where the scale of the signal amplitude (220, 320, 420) is less than the threshold (330, 430). The apparatus according to claim 1, characterized in that the quotient (q1, q2) in each section is calculated from the number (210, 212) and the total number of blocks (210, 212) in each section, and the quotient (q1, q2) in each section is compared to a further threshold (350, 450), or the mathematical combination of the quotients (q1, q2) in several sections is compared to a further threshold (350, 450), and the cardiac state of the human or animal organism is classified into the first class if the majority of the quotients (q1, q2) or the mathematical combination of the quotients (q1, q2) in several sections is greater than the further threshold (350, 450), or the cardiac state of the human or animal organism is classified into the second class if the majority of the quotients (q1, q2) or the mathematical combination of the quotients (q1, q2) in several sections is not greater than the further threshold (350, 450).
  3. The apparatus according to claim 1 or 2, characterized in that the computer-readable program causes the processor (630) to indicate a measure of the probability that the classification of the heart state of the human or animal organism into the first or second class is correct.
  4. The apparatus according to any one of claims 1 to 3, characterized in that the computer-readable program causes the processor (630) to initiate or prevent therapeutic treatment of the human or animal body using the treatment unit of the apparatus according to the classification performed.
  5. The apparatus according to any one of claims 1 to 4, characterized in that the detection unit (620) has at least one sensor used to detect at least one of the following parameters of the human or animal's living body, wherein the parameters include electrical body signals, impedance, pressure, heart sounds, respiratory parameters, position, movement, temperature, blood oxygen saturation, pH value, and biochemical markers.
  6. The apparatus according to any one of claims 1 to 5, characterized in that the computer-readable program causes the processor (630) to adapt the length of the first section of the signal segment (201) and/or further sections of the signal segment (201) as a function of the result determined in the previous step.
  7. The apparatus according to any one of claims 1 to 6, characterized in that the scale (220, 320, 420) of the signal amplitude is selected from the group consisting of maximum-minimum difference, signal variance, and percentile interval.
  8. The apparatus according to any one of claims 1 to 7, characterized in that the computer-readable program causes the processor (630) to use, in addition to the first quotient (q1), further variables for classifying the biological state of the human or animal into the first or second class.
  9. The apparatus according to claim 8, characterized in that the further variables are selected from the heart rate (HR), blood pressure, respiratory rate, and respiratory depth of the human or animal organism.
  10. The apparatus according to any one of claims 1 to 9, characterized in that the computer-readable program causes the processor (630) to subdivide the signal segment (201) into blocks (210, 212) in multiple sections of the signal segment (201), and at least two of the multiple sections overlap each other in terms of region.
  11. The apparatus according to any one of claims 1 to 10, characterized in that the apparatus has at least one heart rate estimator.
  12. The apparatus according to any one of claims 1 to 11, characterized in that the computer-readable program causes the processor (630) to initiate or prevent stimulation of the cardiac region of the human or animal's heart by the stimulation unit, according to the classification performed.
  13. A computer program product having computer-readable code that causes the processor (440) to perform the following steps when executed on the processor (440), wherein the steps are: a) The detection unit (620) of an implantable medical device (600) for detecting cardiac signals from a living human or animal and stimulating the heart of the human or animal detects the cardiac signals (200, 610) of the living human or animal in a time-dependent manner. b) Define a signal segment (201) that extends over an adjustable first time length of the cardiac signal (200, 610), c) The signal segment (201) is subdivided into a first block (210) in at least a first section of the signal segment, d) Determine the total number of the first block (210), e) Determine the signal amplitude scale (220, 320, 420) in each of the first blocks (210), and characterize the signal amplitude as the difference between the minimum signal deflection and the maximum signal deflection within the corresponding block. f) Determine the number of first blocks (210) in which the scale (220, 320, 420) of the signal amplitude is less than a first threshold (330, 430) that can be determined in advance. g) Calculate a first quotient (q1) from the number of first blocks (210) whose scales (220, 320, 420) of the signal amplitude are less than the first threshold (330, 430), and the total number of first blocks (210). h) Compare the first quotient (q1) with the second threshold (350, 450), i) A computer program product comprising the step of classifying the cardiac state of a human or animal organism into a first class if the first quotient (q1) is greater than the second threshold (350, 450), or classifying the cardiac state of a human or animal organism into a second class if the first quotient (q1) is not greater than the second threshold (350, 450), wherein one of the first and second classes represents the physiological state of the organism, and the other of the first and second classes represents the pathophysiological state of the organism, where the physiological state is sinus rhythm or non-tachycardic, and the pathophysiological state is tachycardic.

Description

The present invention relates to an apparatus for detecting signals from a human or animal's biological system using a preamble according to claim 1, a computer program product using a preamble according to claim 14, and a method for signal processing using a preamble according to claim 15. Known solutions for evaluating quasi-periodic signals, such as the electrical signals of cardiac activity in living organisms, are primarily based on pre-programmed event segmentation. This means that specific events must first be extracted from the measured signal in order to then perform an event-based evaluation of the quasi-periodic signal. This implies additional computational work associated with additional power consumption. Furthermore, event segmentation is often prone to failure. For numerous detection devices, especially embedded devices, low power consumption is crucial for ensuring high device durability. Therefore, computationally expensive signal processing steps should be avoided as a general rule. Known block-based signal evaluation methods typically involve computationally intensive signal transformations. Therefore, such methods are largely unsuitable for low-power detection devices. Other known signal evaluation methods use heuristic techniques and/or require initial training using training data to distinguish between different states. Therefore, the robustness of the following distinctions between process data directly depends on the quality of the previously used training data. European Patent EP2448632B1 describes a system having means for receiving a heart sound signal from a heart sound sensor, means for detecting several heart sounds within the heart sound signal, and means for classifying each of the detected heart sounds as either a first classification or a second classification based on one or more characteristics of the detected heart sounds. US2018/00303368A1 describes, in particular, an implantable cardioverter-defibrillator (ICD) that performs a method for determining whether a first criterion for the recognition of ventricular tachyarrhythmia is met by electrical cardiac signals. For this purpose, the ICD determines features from cardiac signal segments and determines whether a first portion of the features meets a monophase waveform criterion and whether a second portion of the features meets a supraventricular impulse criterion. WO2020/049514A1 describes a method for selecting stimulation therapy parameter values, the method comprising the steps of: detecting signals relating to the patient's condition during and/or after a brain stimulation session in which the stimulus is delivered to at least one location in the brain; analyzing the detected signals to quantitatively evaluate the side effects and symptomatic effects of the therapy; and selecting a set of therapy parameter values based on the quantitative evaluation of the side effects and symptomatic effects of the therapy. European Patent No. 2448632U.S. Patent Application Publication No. 2018/00303368International Publication No. 2020/049514 This is a schematic representation of discrete event signal processing as known from conventional technology.This is a section of signal segments subdivided into blocks of the first width.This is the same section of the signal segment as in Figure 2A, but it has been subdivided into blocks with a larger width than in Figure 2A.This is an illustration of cardiac electrical signals originating from the heart and exhibiting sinus rhythm.This is the first graphical representation of the intermediate result of signal processing from the signal shown in Figure 3A.This is a second graphical representation of the intermediate result of signal processing for the signal in Figure 3A.This is a third graphical representation of the intermediate result of signal processing for the signal in Figure 3A.This is a representation of cardiac electrical signals originating from the heart, specifically ventricular fibrillation.This is the first graphical representation of the intermediate result of signal processing from the signal shown in Figure 4A.This is a second graphical representation of the intermediate result of signal processing for the signal in Figure 4A.This is a third graphical representation of the intermediate result of signal processing for the signal in Figure 4A.This is an exemplary decision space for classifying values that reflect a first or second state.This is a block diagram of an exemplary apparatus for detecting signals from a living organism, such as a human or animal. Figure 1 shows an example of a cardiac electrical signal 100 for visualization using discrete event signal processing, as known from prior art. First, individual cardiac cycles are determined in the cardiac signal 100 based on comparison with a first threshold 110. Furthermore, it is customary to use a second threshold 120 to evaluate the statistical characteristics between the first threshold 110 and the second threshold