METHOD FOR DETECTING BRAIN CONDITION STATE AND A PORTABLE DETECTION SYSTEM THEREOF

Abstract

Present disclosure describes the method and system for detecting the brain condition state of a subject. Method comprising calibrating a power zone of the system in real-time and detecting a reflected signal for each of a plurality of transmitted input signals on scanning each of lobe locations of the subject after calibration. Thereafter, method comprising validating an array generated using the plurality of transmitted input signal and corresponding reflected signal for each of the lobe locations and generating a lobe fit value for the validated array using a curve fitting technique. Subsequently, method comprising computing logarithmic ratios corresponding to four pairs of contralateral lobe location, six pairs of ipsilateral lobe locations in left hemisphere and six pairs of ipsilateral lobe locations in right hemisphere using the lobe fit value and classifying the logarithmic ratios into one of brain condition state classes by comparing with pre-labelled logarithmic ratios stored in system.

Description

Claims

1-15. (canceled)

16. A method for detecting brain condition state of a subject, the method comprising: calibrating, by a portable detection system, a power zone of the portable detection system in real-time based on a Signal-to-Noise (S/N) ratio detected from at least one reflected signal received on scanning a part of the subject’s lobe;detecting, by the portable detection system, a reflected signal for each of a plurality of transmitted input signal on scanning each of lobe locations of the subject after calibration;validating, by the portable detection system, an array generated using the plurality of transmitted input signal and corresponding reflected signal for each of the lobe locations based on predetermined threshold condition;generating, by the portable detection system, a lobe fit value for the validated array for each of the lobe locations using a curve fitting technique;computing, by the portable detection system, logarithmic ratios corresponding to four pairs of contralateral lobe location, six pairs of ipsilateral lobe locations in left hemisphere and six pairs of ipsilateral lobe locations in right hemisphere using the lobe fit value generated for the validated array for each of the lobe locations; andclassifying, by the portable detection system, the logarithmic ratios corresponding to four pairs of contralateral lobe location and six pairs of ipsilateral lobe locations into one of brain condition state classes by comparing with pre-labelled logarithmic ratios stored in the portable detection system.

17. The method as claimed in claim 16, wherein the lobe locations comprise of frontal left lobe, frontal right lobe, temporal left lobe, temporal right lobe, parietal left lobe, parietal right lobe, occipital left lobe and occipital right lobe.

18. The method as claimed in claim 16, wherein the calibrating power zone of the portable detection system is performed automatically or manually.

19. The method as claimed in claim 16, wherein the validating the array generated using the plurality of transmitted input signal and corresponding reflected signal for each of the lobe locations based on predetermined threshold condition comprises: generating, by the portable detection system, the array of the plurality of transmitted input signal and corresponding reflected signal for each of the lobe locations;determining, by the portable detection system, whether the reflected signal in the generated array is greater than a minimum predetermined threshold value and less than a maximum predetermined threshold value; andeliminating, by the portable detection system, at least one transmitted input signal and corresponding reflected signal from the generated array of the plurality of transmitted input signal and corresponding reflected signal when the reflected signal is not greater than the minimum predetermined threshold value or not less than the maximum predetermined threshold value.

20. The method as claimed in claim 16, wherein the brain condition state classes comprise no brain hemorrhage and brain hemorrhage.

21. The method as claimed in claim 16, wherein the brain condition state classes comprise no brain hemorrhage, mild brain hemorrhage, moderate brain hemorrhage, and severe brain hemorrhage.

22. The method as claimed in claim 16, further comprising: determining, by the portable detection system, specific brain condition state classes using the logarithmic ratios corresponding to the four pairs of contralateral lobe location, the six pairs of ipsilateral lobe locations in left hemisphere and the six pairs of ipsilateral lobe locations in right hemisphere and Boolean operations,wherein the specific brain condition state classes is one of unilateral hemorrhage, bilateral hemorrhage, and both unilateral and bilateral hemorrhage.

23. The method as claimed in claim 16, further comprising: classifying, by the portable detection system, each of the lobe locations into hemorrhage and not hemorrhage along with condition state confidence score and condition state probability calculated using a classification technique on the logarithmic ratios corresponding to four pairs of contralateral lobe location and six pairs of ipsilateral lobe locations in each hemisphere,wherein the classification technique is a hierarchical tree-based classification technique or a machine learning classification technique.

24. The method as claimed in claim 16, wherein the curve fitting technique is of a least square curve fitting technique.

25. A portable detection system for detecting brain condition state of a subject, the portable detection system comprising: a processor; anda memory communicatively coupled to the processor, wherein the memory stores processor-executable instructions, which on execution, cause the processor to calibrate a power zone of the portable detection system in real-time based on a Signal-to-Noise (S/N) ratio detected from at least one reflected signal received on scanning a part of the subject’s lobe;detect a reflected signal for each of a plurality of transmitted input signal on scanning each of lobe locations of the subject after calibration;validate an array generated using the plurality of transmitted input signal and corresponding reflected signal for each of the lobe locations based on predetermined threshold condition;generate a lobe fit value for the validated array for each of the lobe locations using a curve fitting technique;compute logarithmic ratios corresponding to four pairs of contralateral lobe location, six pairs of ipsilateral lobe locations in left hemisphere and six pairs of ipsilateral lobe locations in right hemisphere using the lobe fit value generated for the validated array for each of the lobe locations; andclassify the logarithmic ratios corresponding to four pairs of contralateral lobe location and six pairs of ipsilateral lobe locations into one of brain condition state classes by comparing with pre-labelled logarithmic ratios stored in the portable detection system.

26. The portable detection system as claimed in claim 25, wherein the lobe locations comprise of frontal left lobe, frontal right lobe, temporal left lobe, temporal right lobe, parietal left lobe, parietal right lobe, occipital left lobe and occipital right lobe.

27. The portable detection system as claimed in claim 25, wherein the curve fitting technique is of a least square curve fitting technique.

28. The portable detection system as claimed in claim 25 is configured to: generate the array of the plurality of transmitted input signal and corresponding reflected signal for each of the lobe locations;determine whether the reflected signal in the generated array is greater than a minimum predetermined threshold value and less than a maximum predetermined threshold value; andeliminate at least one transmitted input signal and corresponding reflected signal from the generated array of the plurality of transmitted input signal and corresponding reflected signal when the reflected signal is not greater than the minimum predetermined threshold value or not less than the maximum predetermined threshold value.

29. The portable detection system as claimed in claim 25, wherein the brain condition state classes comprises no brain hemorrhage and brain hemorrhage.

30. The method as claimed in claim 25, wherein the brain condition state classes comprise no brain hemorrhage, mild brain hemorrhage, moderate brain hemorrhage, and severe brain hemorrhage.

31. The portable detection system as claimed in claim 25 is configured to: determine specific brain condition state classes using the logarithmic ratios corresponding to the four pairs of contralateral lobe location, the six pairs of ipsilateral lobe locations in left hemisphere and the six pairs of ipsilateral lobe locations in right hemisphere and Boolean operations,wherein the specific brain condition state classes is one of unilateral hemorrhage, bilateral hemorrhage, and both unilateral and bilateral hemorrhage.

32. The portable detection system as claimed in claim 25 is configured to: classify each of the lobe locations into hemorrhage and not hemorrhage along with condition state confidence score and condition state probability calculated using a classification technique on the logarithmic ratios corresponding to four pairs of contralateral lobe location and six pairs of ipsilateral lobe locations in each hemisphere,wherein the classification technique is a hierarchical tree-based classification technique or a machine learning classification technique.