Granted Patent B2

US 12,440,124 · App. 16/970,170 · Granted Oct 14, 2025

Inventors: Andrew Knight (Tampere, FI); Kaapo Annala (Tampere, FI)

Assignee: NEURO EVENT LABS OY

A61B5/1128A61B5/4094A61B5/7264A61B5/7405A61B5/742G06V10/764G06V10/82G06V20/46G06V40/20G06V20/44

Abstract

Claims (115)

1. A method for detecting a motor seizure type of an epileptic seizure of a patient, said method to be performed by a patient monitoring system comprising a processor operatively connected with

a video camera,

a depth sensor or a stereoscopic imaging equipment,

an audio sensor, and

one or more databases comprising a first pre-trained neural network, a second pre-trained neural network and a third pre-trained neural network, the method comprising:

receiving, by the video camera, video data of the patient;

detecting, by an apparatus operatively connected to the video camera, a first anomaly in the video data as anomaly in movement of the patient, wherein the first anomaly in the video data is detected based on the video data, and the detecting comprises:

determining, by the apparatus, an actual feature over a video data segment, the actual feature representing actual movement of the patient;

determining, by the apparatus, a predicted feature over the video data segment using the first pre-trained neural network, wherein the first pre-trained neural network is pre-trained for normal sleeping data captured from persons sleeping without any seizures, the predicted feature representing predicted movement of the patient;

determining, by the apparatus, a difference between the actual feature and the predicted feature; and

registering, by the apparatus, the actual feature as the first anomaly based on the difference; and the method comprises:

determining, by the apparatus, a video frame stack comprising the first anomaly;

classifying, by the apparatus, the video frame stack using the first pre-trained neural network, that is a pre-trained image neural network, to obtain a first classification, wherein the first classification is based on a seizure classification for epileptic seizures;

receiving audio data from the audio sensor configured to detect sounds produced by the patient over time;

transforming the audio data to obtain a two-dimensional representation of the audio data;

determining a second anomaly in the two-dimensional representation of the audio data as anomaly in sounds produced by the patient based on the audio data or based on the detected first anomaly;

determining an audio clip comprising the second anomaly;

classifying the audio clip using the second pre-trained neural network, that is a pre-trained audio neural network, to obtain a second classification, wherein the second classification is based on the seizure classification for epileptic seizures;

receiving depth data from the stereoscopic imaging equipment or the depth sensor configured to detect movement of the patient over time;

detecting a third anomaly in the depth data as anomaly in movement of the patient;

determining a depth data frame stack comprising the third anomaly;

classifying the depth data frame stack using the third pre-trained neural network, that is a pre-trained depth neural network, to obtain a third classification, wherein the third classification is based on the seizure classification for epileptic seizures; and

determining, by the apparatus, the motor seizure type based on the first classification, the second classification, and the third classification by:

applying pooling to respective outputs of the pre-trained image neural network, the pre-trained audio neural network, and the pre-trained depth neural network to obtain a combined output;

determining the motor seizure type based on the combined output; and

generating, for display on a graphical user interface of a user device operatively connected to the processor, an interactive report comprising the motor seizure type determined based on the combined output, wherein the interactive report further comprises a plurality of motor seizure types classified by the patient monitoring system.

2. The method according to claim 1 , wherein the first anomaly in the video data is detected if intensity of the audio data exceeds a pre-defined threshold.

3. The method according to claim 1 , wherein the third anomaly in the depth data is detected based on the depth data, and the detecting comprises:

determining an actual feature over a depth data segment, the actual feature representing actual movement of the patient;

determining a predicted feature over the depth data segment using a pre-trained neural network that is pre-trained for normal depth data during sleeping, the predicted feature representing predicted movement of the patient;

determining a difference between the actual feature and the predicted feature; and

registering the actual feature as the third anomaly based on the difference.

4. The method according to claim 1 , wherein the third anomaly in the depth data is detected based on the detected first anomaly or based on the second anomaly.

5. An apparatus for detecting a motor seizure type of an epileptic seizure of a patient, said apparatus comprising at least one processor, operatively connected with

a video camera,

a depth sensor or a stereoscopic imaging equipment,

an audio sensor, and

one or more databases comprising a first pre-trained neural network, a second pre-trained neural network and a third pre-trained neural network; and memory including computer program code, wherein the memory and the computer program code are configured to, with the at least one processor, cause the apparatus to perform:

receiving, by the apparatus from the video camera operatively connected to the apparatus, video data captured by the video camera of the patient;

detecting, by the apparatus, a first anomaly in the video data as anomaly in movement of the patient, wherein the first anomaly in the video data is detected based on the video data, and the detecting comprises:

determining, by the apparatus, an actual feature over a video data segment, the actual feature representing actual movement of the patient;

determining, by the apparatus, a predicted feature over the video data segment using a first pre-trained neural network, wherein the first pre-trained neural network is pre-trained for normal sleeping data captured from persons sleeping without any seizures, the predicted feature representing predicted movement of the patient;

determining, by the apparatus, a difference between the actual feature and the predicted feature; and

registering, by the apparatus, the actual feature as the first anomaly based on the difference; and wherein the memory and the computer program code are configured to, with the at least one processor, cause the apparatus to perform:

determining, by the apparatus, a video frame stack comprising the first anomaly;

classifying, by the apparatus, the video frame stack using the first pre-trained neural network, that is a pre-trained image neural network, to obtain a first classification, wherein the first classification is based on a seizure classification for epileptic seizures;

receiving audio data from the audio sensor configured to detect sounds produced by the patient over time;

transforming the audio data to obtain a two-dimensional representation of the audio data;

determining a second anomaly in the two-dimensional representation of the audio data as anomaly in sounds produced by the patient based on the audio data or based on the detected first anomaly;

determining an audio clip comprising the second anomaly;

classifying the audio clip using the second pre-trained neural network, that is a pre-trained audio neural network, to obtain a second classification, wherein the second classification is based on the seizure classification for epileptic seizures;

receiving depth data from the stereoscopic imaging equipment or the depth sensor configured to detect movement of the patient over time;

detecting a third anomaly in the depth data as anomaly in movement of the patient;

determining a depth data frame stack comprising the third anomaly;

classifying the depth data frame stack using the third pre-trained neural network, that is a pre-trained depth neural network, to obtain a third classification, wherein the third classification is based on the seizure classification for epileptic seizures; and

determining, by the apparatus, the motor seizure type based on the first classification, the second classification, and the third classification by

applying pooling to respective outputs of the pre-trained image neural network, the pre-trained audio neural network, and the pre-trained depth neural network to obtain a combined output;

determining the motor seizure type based on the combined output; and

generating, for display on a graphical user interface of a user device operatively connected to the processor, an interactive report comprising the motor seizure type determined based on the combined output, wherein the interactive report further comprises a plurality of motor seizure types classified by the apparatus.

6. A system for detecting a motor seizure type of an epileptic seizure of a patient, comprising

a video device;

an audio sensor configured to detect sounds produced by the patient over time;

stereoscopic imaging equipment or a depth sensor configured to detect movement of the patient over time; and

an apparatus operatively connected to the video device, the audio sensor, the stereoscopic imaging equipment or the depth sensor, and one or more databases comprising a first pre-trained neural network, a second pre-trained neural network and a third pre-trained neural network, said apparatus comprising at least one processor, memory including computer program code, the memory and the computer program code configured to, with the at least one processor, cause the system to perform

receiving, by the apparatus, video data captured by the video device of the patient;

detecting, by the apparatus, a first anomaly in the video data as anomaly in movement of the patient; wherein the first anomaly in the video data is detected based on the video data, and the detecting comprises:

determining, by the apparatus, an actual feature over a video data segment, the actual feature representing actual movement of the patient;

determining, by the apparatus, a predicted feature over the video data segment using a first pre-trained neural network, wherein the first pre-trained neural network is pre-trained for normal sleeping data captured from persons sleeping without any seizures, the predicted feature representing predicted movement of the patient;

determining, by the apparatus, a difference between the actual feature and the predicted feature; and

registering, by the apparatus, the actual feature as the first anomaly based on the difference; wherein the memory and the computer program code are configured to, with the at least one processor, cause the system to perform:

determining, by the apparatus, a video frame stack comprising the first anomaly;

classifying the video frame stack using the first pre-trained neural network, that is a pre-trained image neural network, to obtain a first classification, wherein the first classification is based on a seizure classification for epileptic seizures;

receiving audio data from the audio sensor;

transforming the audio data to obtain a two-dimensional representation of the audio data;

determining a second anomaly in the two-dimensional representation of the audio data as anomaly in sounds produced by the patient based on the audio data or based on the detected first anomaly;

determining an audio clip comprising the second anomaly;

classifying the audio clip using the second pre-trained audio neural network, that is a pre-trained audio neural network, to obtain a second classification, wherein the second classification is based on the seizure classification for epileptic seizures;

receiving depth data from the stereoscopic imaging equipment or the depth sensor;

detecting a third anomaly in the depth data as anomaly in movement of the patient;

determining a depth data frame stack comprising the third anomaly;

classifying the depth data frame stack using the third pre-trained neural network, that is a pre-trained depth neural network, to obtain a third classification, wherein the third classification is based on the seizure classification for epileptic seizures; and

determining, by the apparatus, the motor seizure type based on the first classification, the second classification, and the third classification by:

applying pooling to respective outputs of the pre-trained image neural network, the pre-trained audio neural network, and the pre-trained depth neural network to obtain a combined output;

determining the motor seizure type based on the combined output; and

generating, for display on a graphical user interface of a user device operatively connected to the processor, an interactive report comprising the motor seizure type determined based on the combined output, wherein the interactive report further comprises a plurality of motor seizure types classified by the apparatus.

7. The system according to claim 6 , wherein the first anomaly in the video data is detected if intensity of the audio data exceeds a pre-defined threshold.

8. The system according to claim 6 , wherein the third anomaly in the depth data is detected based on the depth data, and the detecting comprises:

determining an actual feature over a depth data segment, the actual feature representing actual movement of the patient;

determining a predicted feature over the depth data segment using a pre-trained neural network that is pre-trained for normal depth data during sleeping, the predicted feature representing predicted movement of the patient;

determining a difference between the actual feature and the predicted feature; and

registering the actual feature as the third anomaly based on the difference.

9. The system according to claim 6 , wherein the third anomaly in the depth data is detected based on the detected first anomaly or based on the second anomaly.

10. A computer program product embodied on a non-transitory computer readable medium for detecting a motor seizure type of an epileptic seizure of a patient, said computer program product comprising computer program code configured to, when executed on at least one processor of a system operatively connected to a video camera, a depth sensor or a stereoscopic imaging equipment,

an audio sensor, and

one or more databases comprising a first pre-trained neural network, a second pre-trained neural network and a third pre-trained neural network, cause the system to perform:

receiving, by the system, video data captured by the video camera of the patient;

detecting, by the system, a first anomaly in the video data as anomaly in movement of the patient, wherein the first anomaly in the video data is detected based on the video data, and the detecting comprises:

determining, by the system, an actual feature over a video data segment, the actual feature representing actual movement of the patient;

determining, by the system, a predicted feature over the video data segment using a first pre-trained neural network, wherein the first pre-trained neural network is pre-trained for normal sleeping data captured from persons sleeping without any seizures, the predicted feature representing predicted movement of the patient;

determining, by the system, a difference between the actual feature and the predicted feature; and

registering, by the system, the actual feature as the first anomaly based on the difference; and wherein said computer program product comprising computer program code configured to, when executed on the at least one processor, cause the system to perform:

determining, by the system, a video frame stack comprising the first anomaly;

classifying, by the system, the video frame stack using the first pre-trained neural network, that is a pre-trained image neural network, to obtain a first classification, wherein the first classification is a seizure classification for epileptic seizures;

receiving audio data from the audio sensor configured to detect sounds produced by the patient over time;

transforming the audio data to obtain a two-dimensional representation of the audio data;

determining a second anomaly in the two-dimensional representation of the audio data as anomaly in sounds produced by the patient based on the audio data or based on the detected first anomaly;

determining an audio clip comprising the second anomaly;

classifying the audio clip using the second pre-trained audio neural network, that is a pre-trained audio neural network, to obtain a second classification, wherein the second classification is based on the seizure classification for epileptic seizures;

receiving depth data from the stereoscopic imaging equipment or the depth sensor configured to detect movement of the patient over time;

detecting a third anomaly in the depth data as anomaly in movement of the patient; determining a depth data frame stack comprising the third anomaly;

classifying the depth data frame stack using the third pre-trained depth neural network, that is a pre-trained depth neural network, to obtain a third classification, wherein the third classification is based on the seizure classification for epileptic seizures; and

determining, by the system, a motor seizure type based on the first classification, the second classification, and the third classification by:

applying pooling to respective outputs of the pre-trained image neural network, the pre-trained audio neural network, and the pre-trained depth neural network to obtain a combined output;

determining the motor seizure type based on the combined output;

generating, for display on a graphical user interface of a user device operatively connected to the processor, an interactive report comprising the motor seizure type determined based on the combined output, wherein the interactive report further comprises a plurality of motor seizure types classified by the system.

Assignments (1)

Priority Claims (1)

Continuity (1)

References Cited (22)