A dumb-proof defibrillator trainer system

Abstract

A dumb-proof defibrillator trainer system (100) is disclosed, comprising an ECG reader (12) configured to receive analog signals from bio-signal sources and digital ECG waveform data from a database to generate a reference waveform. A waveform conversion and scaling module (14) includes an ADC (16), a scaling engine (18) for dynamic amplitude mapping to display coordinates, and a segmenter (20) for clinically significant interval partitioning. A selector (22) chooses a target waveform for comparison. A comparator subsystem (26) executes waveform morphing via a pointwise compensation algorithm (26a) and / or a derivative-integral deviation algorithm (26b) based on slope and curvature. A signal embedding module (28) introduces real-world artifacts. A defibrillator simulation interface (30) mimics clinical UI without high-voltage circuitry. The system enables acquisition, segmentation, transformation, artifact injection, and safe simulated defibrillation, transforming the reference waveform to match the target morphology while preserving training accuracy and eliminating shock risk.

Description

[0001] A DUMB-PROOF DEFIBRILLATOR TRAINER SYSTEM

[0002] FIELD OF THE INVENTION:

[0003] This invention relates to the field of biomedical engineering.

[0004] Particularly, this invention relates to a dumb-proof defibrillator trainer system.

[0005] BACKGROUND OF THE INVENTION:

[0006] In the medical domain, training of staff (doctors, nurses, technicians) is of utmost importance. This enables medical staff, doctors, nurses, emergency technicians, etc.) to keep themselves abreast of latest diagnoses and treatment protocols that are frequently updated by certification organisations.

[0007] One such treatment protocol is advanced cardiac life support specified by American Heart Association. They have specified an algorithm (flow chart) to treat patients that show up with different cardiac arrhythmias to hospital ER or in operation theatre and ICUs. These algorithms are treated as gold standards in concerned treatments and certifications are provided to the medical staff who demonstrate proficiency levels in these.

[0008] So, the appropriate medical staff are trained using simulators that create / mimic various arrhythmia scenarios.

[0009] In prior arts, there are two types of simulators that are used:

[0010] A. A first type of simulator consists of a box that has a set of preprogrammed ECG rhythms for various arrhythmia, a medical manikin (full body dummy), and a defibrillator / monitor that can read ECG and administer shock treatment as per prescribed algorithm mentioned above.

[0011] Typical use case of a simulator: Arrythmia box containing pre-programmed ECG rhythms is connected to a manikin that can bring the output of the box to the manikin’s chest in order to simulate a real patient body with cardiac signals on its skin surface. These signals can be read through small metallic pads on the manikin’s chest when an ECG monitor or defibrillator cable is attached to it. Then, on the box, if a shockable rhythm option is chosen, and an ECG cable is connected between the dummy and the monitor / defibrillator, then the screen will show that chosen waveform. A trainee can then use the defibrillator paddles to deliver a real shock to the chest of the manikin.

[0012] B. A second type of a simulator is a software-only type wherein a tablet PC has an application software with a user interface that mimics a patient monitor or a defibrillator. This software has pre-programmed ECG rhythms that can be displayed on the tablet screen through the user menu. The same menu has provision to simulate a typical defibrillator user menu consisting of selection of required Joules of energy, and charge and discharge buttons. So, the entire treatment protocol is executed on the tablet PC without any physical dummy or defibrillator paddles.

[0013] According to prior arts, for the first type of simulator, the following deficiencies / problems were observed:

[0014] 1. This prior art simulator is quite bulky consisting of three separate pieces (manikin, ECG box, and real defibrillator) of hardware connected to each other to simulate a required scenario. It consumes large space. 2. Each of these pieces are hardwired to each other through external cables and connectors that are prone to getting damaged or come off during training of a batch of multiple personnel at a time.

[0015] 3. Use of a real defibrillator to read ECG signals and deliver shock undermines safety of the user (i.e. the trainees who are not fully proficient in use of high voltage defibrillators) and are exposed to possibilities of high voltage shock to their own bodies during training. This issue is severe when the training batch size is large.

[0016] 4. The pre-programmed rhythms are completely digital in nature. The software, for these types of prior arts, is not capable of taking inputs of a real analog voltage signal of a patient ECG.

[0017] 5. The overall solution is not customizable as only fixed rhythms are available to show during training. No modification of rhythms or its parameters is possible once device is programmed.

[0018] According to prior arts, for the second type of simulator, the following deficiencies / problems were observed:

[0019] 1. Use of Tablet PC to show simulations is not an effective training aid as it is, merely, shown entirely on a tablet screen without requiring any device for actual physical use. Doctors / nurses and technicians need to be trained in the actual / real use of devices as real emergency situation in clinical setting involves real patients and real devices. This is not possible with tablet-based simulators. Since the “touch and feel” of a real device and patient is not there, the trained staff are prone to make mistakes in real situations when faced. 2. The pre-programmed rhythms are completely digital in nature. The software is not capable of taking inputs of a real analog voltage signal of a patient ECG into the tablet PC.

[0020] 3. The overall solution is not customizable as only fixed rhythms are available to show during training. No modification of rhythms or its parameters is possible once device is programmed.

[0021] OBJECTS OF THE INVENTION:

[0022] An object of the invention is to combine functionality of an ECG rhythm generator box and defibrillator inside one single device, this device being a trainer system device.

[0023] Another object of the invention is to provide a compact, smart, and robust defibrillator trainer system device.

[0024] Yet another object of the invention is to provide a defibrillator trainer system device which has actual “touch and feel”, feels real, is still a simulation, yet does not deliver real shocks, and works with dumb or any mannequins.

[0025] Still another object of the invention is to provide a defibrillator trainer system device which eliminates the possibilities of shock to users whilst training.

[0026] An additional object of the invention is to provide a defibrillator trainer system device which is capable of taking inputs of a real analog voltage signal of a patient ECG. Yet an additional object of the invention is to provide a defibrillator trainer system device which is customizable as only fixed rhythms are available to show during training.

[0027] Still additional object of the invention is to provide a defibrillator trainer system device such that modification of rhythms or its parameters is possible once device is programmed.

[0028] SUMMARY OF THE INVENTION:

[0029] According to this invention, there is provided a dumb-proof defibrillator trainer system comprising:

[0030] • an ECG reader configured to receive both analog ECG signals from an external bio- signal source and digital ECG waveform data from an ECG database, wherein the analog ECG signals or the digital ECG waveform data are processed to generate a reference waveform representing an initial ECG signal;

[0031] • a waveform conversion and scaling module coupled to said ECG reader, the said conversion and scaling module comprising: o an analog-to-digital converter (ADC) for digitizing received analog ECG signals to produce the reference waveform in a digital scale; o a scaling engine configured to map the reference waveform to a pixel coordinate system of a display monitor, wherein the scaling engine calculates a dynamic scaling factor based on maximum and minimum signal amplitudes within a clinically relevant time window to ensure morphological fidelity of the waveform irrespective of its original amplitude; o a segmenter configured to partition the reference waveform into multiple clinically significant waveform intervals based on cardiac morphological features, using a range qualifier correlative to these physiological components; o a selector configured to select a target waveform, representing a desired ECG morphology to be shown on the display monitor and wherein the same segmenter is configured to partition the target waveform into the same number and type of clinically significant intervals as the reference waveform o a comparator subsystem configured to execute a waveform morphing operation by comparing each interval of the reference waveform to a target waveform, wherein said comparator employs:

[0032] ■ a pointwise compensation algorithm configured to compute amplitude deviations at discrete sampling points within each interval and apply compensatory adjustments to the reference waveform, and / or

[0033] ■ a derivative-integral deviation algorithm configured to compute waveform deviation functions based on slope, curvature, and signal envelope characteristics between the reference waveform and the target waveform, to enable morphology transformation; and

[0034] • a signal embedding module communicably coupled to the waveform conversion and scaling module, the signal embedding module configured to inject real- world signal artifacts into the reference waveform or the target waveform;

[0035] • a defibrillator simulation interface (30) coupled to the waveform conversion and scaling module (14), the defibrillator simulation interface (30) configured to replicate user interface functions of a clinical defibrillator without any high-voltage generation or delivery circuitry; and wherein the defibrillator trainer system (100) is configured to perform ECG waveform acquisition, scaling, interval-based segmentation, waveform transformation based on deviation between the reference waveform and the target waveform, artifact embedding, and simulated defibrillation, wherein the reference waveform is transformed to match the target waveform without delivering physical electric shock.

[0036] In at least an embodiment, the comparator subsystem includes a dual-mode computation engine configured to selectively operate between the pointwise compensation algorithm and the derivative-integral deviation algorithm based on waveform complexity metrics.

[0037] In at least an embodiment, said system comprising a biomarker simulation module configured to generate and synchronize physiological signals and biomarkers with the ECG waveform, for multimodal clinical scenario simulation, said biomarker simulation module dynamically correlates ECG waveform morphological changes to synchronized changes in at least one biomarker.

[0038] In at least an embodiment, said waveform morphing process comprises adjusting waveform intervals through adaptive scaling based on slope analysis and curvature deviation thresholds.

[0039] In at least an embodiment, the ECG reader is configured to ingest analog ECG signals from an external bio-signal source comprising an actual patient. In at least an embodiment, the ECG reader is further configured to ingest digital ECG waveform data from an ECG database.

[0040] In at least an embodiment, the ECG reader comprises a signal selector configured to toggle between ingestion of analog ECG signals and digital ECG waveform data.

[0041] In at least an embodiment, the analog-to-digital converter is further configured to convert analog ECG data from an ECG database into a digital scale for waveform processing.

[0042] In at least an embodiment, the segmenter is further configured to partition the reference waveform into a minimum of five distinct intervals using a range qualifier correlated to clinically significant ranges of ECG waveform data. the comparator subsystem is further configured to compare each interval of a target waveform with a corresponding interval of the reference waveform, irrespective of amplitude scaling between the two waveforms.

[0043] In at least an embodiment, the scaling engine is further configured to apply a common scaling factor to both the reference waveform and the target waveform prior to interval-wise comparison by the comparator subsystem.

[0044] In at least an embodiment, the comparator subsystem is configured to compute deviation between the reference waveform and the target waveform based on comparison of corresponding intervals. In at least an embodiment, the comparator subsystem comprises a divider module configured to subdivide each clinically significant waveform interval into a plurality of discrete sampling points based on a resolution parameter determined by a desired waveform comparison fidelity.

[0045] In at least an embodiment, the comparator subsystem further comprises a second comparator configured to compute amplitude differences between corresponding discrete sampling points of a reference waveform and a target waveform.

[0046] In at least an embodiment, the comparator subsystem further comprises a first computation module configured to generate a compensation matrix based on the amplitude differences computed by the second comparator.

[0047] In at least an embodiment, the compensation matrix is configured to algebraically adjust the reference waveform pointwise to conform to the target waveform.

[0048] In at least an embodiment, the pointwise compensation algorithm executed by the comparator subsystem is configured to compute amplitude deviations at each discrete sampling point within each interval and apply the corresponding compensation to the reference waveform while preserving signal integrity.

[0049] In at least an embodiment, the pointwise compensation algorithm is configured to enable precise morphological recreation of clinically significant target rhythms including but not limited to multifocal atrial tachycardia and hyperkalemia-induced sine wave patterns.

[0050] In at least an embodiment, the comparator subsystem comprises a divider module configured to subdivide each clinically significant waveform interval into a plurality of discrete sampling points based on a resolution parameter determined by waveform comparison requirements.

[0051] In at least an embodiment, the comparator subsystem further comprises a second computation module configured to compute first-order derivatives between each pair of consecutive discrete sampling points of the reference waveform and the target waveform.

[0052] In at least an embodiment, the second computation module is further configured to compute a double integral of the first-order derivatives to reconstruct signal envelope deviations representing macro-morphological differences between the reference waveform and the target waveform.

[0053] In at least an embodiment, the second computation module is configured to generate a reference array and a target array representing derivative and integral computations of the reference waveform and the target waveform respectively.

[0054] In at least an embodiment, the comparator subsystem further comprises a third comparator configured to perform an element-wise comparison of the reference array and the target array to compute deviation values for each discrete sampling point.

[0055] In at least an embodiment, the deviation values computed by the third comparator are configured to drive waveform morphing operations such that the reference waveform is transformed to match the target waveform in terms of slope, curvature, and fidelity. In at least an embodiment, the waveform conversion and scaling module comprises a first computational layer configured to perform analog-to-digital conversion of analog ECG signals within a voltage range of ±2.5 millivolts using a high- precision analog-to-digital converter (ADC) with a predefined resolution.

[0056] In at least an embodiment, the waveform conversion and scaling module comprises a second computational layer configured to segment the scaled waveform into multiple clinically significant waveform intervals including a P-wave, QRS complex, ST-segment, and T-wave.

[0057] In at least an embodiment, the second computational layer is configured to calculate a range qualifier for each waveform interval, the range qualifier representing signal variance, slope, and curvature within that interval.

[0058] In at least an embodiment, the segmenter of the waveform conversion and scaling module is configured to perform interval segmentation aligned with cardiac electrophysiological components comprising a P-wave, QRS complex, ST- segment, and T-wave.

[0059] In at least an embodiment, the pointwise compensation algorithm executed by the comparator subsystem is configured to enable precise amplitude alignment and time alignment between a reference waveform and a target waveform.

[0060] In at least an embodiment, said defibrillator trainer system comprises a defibrillator simulation interface coupled to the waveform conversion and scaling module as also being coupled to a transcutaneous pacing simulation module that provides simulation of transcutaneous pacing based on the target waveform. According to this invention, there is provided a method for operating a dumb-proof defibrillator trainer system, the method comprising:

[0061] • acquiring an ECG signal from either (i) an analog bio- signal input via an analog-to-digital converter (ADC) or (ii) a digital ECG waveform from an ECG database or an ECG simulator device having preprogrammed rhythms;

[0062] • converting the acquired ECG signal into a reference waveform, wherein the reference waveform represents an initial ECG signal derived from either the analog ECG input or the digital waveform, and wherein the converting step comprises signal digitization and amplitude-to-pixel scaling using a dynamic scaling factor calculated based on maximum and minimum signal amplitudes within a clinically relevant time window;

[0063] • selecting a target waveform representing a desired ECG morphology to be displayed on a graphical user interface, wherein the target waveform is retrieved from the ECG database;

[0064] • segmenting both the reference waveform and the target waveform into a plurality of clinically significant waveform intervals based on cardiac morphological features, using a range qualifier correlative to said morphological features;

[0065] • applying a waveform morphing process comprising: o calculating pointwise amplitude deviations between corresponding discrete sampling points of the reference waveform and the target waveform within each interval, and applying compensatory adjustments to the reference waveform; o computing deviation functions based on slope, curvature, and signal envelope characteristics by performing a derivative-integral deviation process, including calculating first-order derivatives between consecutive points, double integrals for envelope reconstruction, and second-order derivatives for curvature fidelity; o generating a compensated waveform by transforming the reference waveform to match the morphology of the target waveform while preserving amplitude integrity and morphological fidelity;

[0066] • embedding simulated signal artifacts, including AC line noise, baseline wander, motion-induced artifacts, and lead detachment artifacts, into the compensated waveform;

[0067] • displaying the compensated waveform on the graphical user interface, wherein the display further overlays biomarker indicators; and

[0068] • activating a simulated defibrillator interface to enable user interaction with virtual controls for energy selection, charge, and discharge, without generation or delivery of high-voltage electrical output.

[0069] BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS:

[0070] The invention will now be described in relation to the accompanying drawings, in which:

[0071] FIGURE 1 illustrates a schematic block diagram of the a dumb-proof defibrillator trainer system of this invention;

[0072] FIGURE 3a illustrates an analog-to-digital converter (ADC) (16) of the system (100) of this invention;

[0073] FIGURE 3b illustrates a scaling engine (18) of the system (100) of this invention; and

[0074] FIGURE 3c illustrates a segmenter (20) of the system (100) of this invention. DETAILED DESCRIPTION OF THE ACCOMPANYING DRAWINGS:

[0075] According to this invention, there is provided a dumb-proof defibrillator trainer system.

[0076] Typically, the dumb-proof defibrillator trainer system combines functionalities of ECG rhythm generator box and a defibrillator in a single device.

[0077] FIGURE 1 illustrates a schematic block diagram of the a dumb-proof defibrillator trainer system of this invention.

[0078] FIGURE 2 illustrates use of this invention’s dumb-proof defibrillator trainer system with a live user or a mannikin.

[0079] Reference numeral 100 refers to the system of this invention.

[0080] Reference numeral 200 refers to a live user or a mannikin on which the system of this invention can be used.

[0081] In at least an embodiment, an ECG database comprises pre-recorded ECG signals and ECG rhythms. Typically, this database is a comprehensive library of ECG waveforms, including normal rhythms, shockable rhythms (e.g., ventricular fibrillation, pulseless VT), and non-shockable rhythms (e.g., asystole, PEA)

[0082] Typically, this trainer system can be used on any used on any regular manikin unlike prior art which requires defibrillation manikin capable of absorbing / dissipating high voltage charges. It could be used on even live human being because it does not deliver a real high-voltage shock. This makes the training as real as possible. Manikin can be eliminated also by reading the rhythms and simulating the defibrillator shock on real patients; thereby, creating a real-life field experience scenario. This was achieved by getting rid of high voltage actuation switches and components as well as accumulators yet maintaining the triggering logic of the high voltage circuits and the related user interface of high voltage operation. Since high voltage does not exist in the present invention, any manikin can be used including live humans also. In prior arts, since real defibrillator is used, a manikin capable of absorbing and dissipating the high voltage charge is required.

[0083] In at least an embodiment, there is provided a configurable, programmable, ECG rhythm generator. On a display, ECG rhythms can be flashed and changed using remote control, voice commands, or the like. Typically, the configurable, programmable, ECG rhythm generator is communicably coupled to the ECG database.

[0084] In at least an embodiment, there is provided an ECG reader (12) is configured to read ECG inputs from either a real patient to whom ECG leads are connected or pre-programmed ECG rhythms from the configurable, programmable, ECG rhythm generator. Typically, this ECG reader (12) is capable of ingesting both analog ECG signals from actual patients and digital ECG signals from internal or external databases. This dual capability is a departure from prior art, which was limited to fixed digital patterns output via Digital to Analog converters (DACs).

[0085] The current invention’s dumb-proof defibrillator trainer system demonstrates full functionality of a defibrillator without discharging high voltages; thereby, making the solution inherently safer. In at least an embodiment, an embedding module (28) is configured to embed ECG signals and ECG rhythms with one or more additional signals such as AC line noise signal, movement artefact signal, loose lead artefact signal, baseline wander signal, and the like signals; in order to train users in terms of real- world signals which are never clean signals. Typically, this embedding module (28) superimposes real-world artifacts onto ECG signals, such as line noise, baseline wander, and motion artifacts, which are absent in prior art training simulators.

[0086] In at least an embodiment, there is provided an annotator mechanism, coupled with said ECG database, said ECG rhythm generator, said ECG reader (12) configured to allow a user to annotate various ECG rhythms.

[0087] In at least an embodiment, there is provided a display mechanism (24), coupled with said ECG database, said ECG rhythm generator, said ECG reader (12), and said annotator mechanism; in order to show ECG signals, ECG rhythms, names of ECG rhythms, and various annotations.

[0088] In at least an embodiment, the ECG rhythm generator, the ECG reader (12), and the annotator mechanism, is coupled with a waveform conversion and scaling module (14).

[0089] FIGURE 3a illustrates an analog-to-digital converter (ADC) (16) of the system (100) of this invention.

[0090] FIGURE 3b illustrates a scaling engine (18) of the system (100) of this invention.

[0091] FIGURE 3c illustrates a segmenter (20) of the system (100) of this invention. In at least an embodiment, an Analog-to-Digital Converter (16) is configured to read analog data from an ECG database, and convert these into a digital scale. A scaling engine (18) is configured to fit the derived digital scale to a display monitor (24) as per the pixel coordinates of the display monitor (24); in order to obtain a pixel-wise waveform data plot based on analog value as input; this waveform is called as the reference waveform. A segmenter module (20) is configured to divide the reference waveform into multiple intervals using a range qualifier correlative to clinically significant ranges (P-wave, QRS complex, ST- segment, T-wave) of ECG waveform data required for clinical decision making. Typically range qualifiers will create minimum of five distinct intervals in the entire data range. However multiple intervals more than five are also possible and present invention includes all possible intervals as required. The system chooses a target waveform which is to be shown using this training device on the display; this target waveform needs to be recreated using the aforementioned reference waveform of this invention. To achieve this, the same aforementioned range qualifier is used to divide the target waveform into the same number of intervals as done on the reference waveform. A first comparator (26) compares each interval of the target waveform with that of the reference waveform. The comparison method, employed by the first comparator (26), is valid irrespective of the scale of the two waveforms (reference waveform, target waveform) being compared. A common scaling engine (18) may be employed in order to bring both waveforms (reference waveform, target waveform) to a common scale before comparison.

[0092] Real ECG signals have variable amplitude, drift, noise, and morphology. Prior arts are rigid; offering fixed templates without dynamic adaptation to real-world

[0093] Y1 variability. This invention’s analog-to-digital conversion pipeline ensures ingestion of continuous ECG voltages, enabling use with real patient outputs or simulators.

[0094] The segmentation algorithm mirrors physiological boundaries (P-wave start to end, QRS onset to offset, etc.), enabling precise manipulation of each waveform part rather than treating it as a generic signal array.

[0095] According to a first module and method, a divider module is configured to divide each of the intervals into further discrete points. The number of discrete points is estimated based on the resolution necessary to compare the two waveforms (reference waveform, target waveform). Once the discrete points are established for both waveforms (reference waveform, target waveform), a second comparator (26) compares two points (one from each waveform) and a first computation module is employed, to run a first set of computations, such that computed values are obtained for each of the aforementioned discrete points and so that, eventually, as an output, the reference waveform is compensated by a value equal to the computed value such that it matches the target waveform.

[0096] Essentially, this first module is a pointwise compensation module (26a):

[0097] • each interval is subdivided into discrete sampling points.

[0098] • the difference in ordinate (amplitude) between corresponding points of the reference waveform (input signal) and the target waveform (desired rhythm) is computed.

[0099] • a compensation matrix is constructed where the reference waveform is algebraically adjusted pointwise to morph into the target waveform while preserving signal integrity. The pointwise compensation algorithm (26a) compares amplitude differences at discrete time points and adjusts reference waveforms; accordingly allowing precise recreation of target rhythms like multifocal atrial tachycardia or hyperkalemia- induced sine waves.

[0100] According to a second module and method, a divider module is configured to divide each of the intervals into further discrete points. The number of discrete points is estimated based on the resolution necessary to compare the two waveforms (reference waveform, target waveform). Once the discrete points are established for both waveforms (reference waveform, target waveform), two consecutive points of the discrete points are taken and a second computation module is employed, to run a second set of computations, such that computed values are obtained for each of the aforementioned discrete points and so that, eventually, as an output, two arrays (reference array, target array) are built. A third comparator (26) is configured to compare each value from the two arrays (reference array, target array) to determine deviation and, then, to use the deviation to obtain the target waveform as a deviation from the reference waveform.

[0101] Essentially, this second module is a derivative-integral deviation module (26b):

[0102] • for each pair of consecutive points, first-order derivatives (slope) are calculated.

[0103] • double integral computations are applied to reconstruct signal envelope deviations, capturing macro-morphological differences.

[0104] • this is followed by second-order derivative checks for signal fidelity against abrupt morphological transitions such as QRS spikes or fibrillation chaos. arrays representing both the reference and target waveforms are compared, and deviations are used to drive waveform morphing algorithms.

[0105] The derivative-integral deviation algorithm (26b) captures slope and curvature differences between waveforms; essential for morphologies with subtle yet clinically significant changes

[0106] Prior arts do not implement this aforementioned multi-level signal transformation mechanism combining derivative-integral math and pointwise compensation to reconstruct ECG rhythms.

[0107] The following steps form the logic for the waveform conversion and scaling module (14):

[0108] 1. Read analog data from an ECG database.

[0109] 2. Values of waveform may be from negative to positive integer or float values (according to some embodiments, in the range of ±2.5 millivolts).

[0110] 3. Convert these analog values to digital scale by Analog-to-Digital (A / D) conversion.

[0111] 4. Fit the range of digital values to a display monitor device (24) as per its pixel coordinates by using a suitable scaling factor.

[0112] 5. This will give a pixel- wise waveform data plot based on analog value as input; this waveform is called as the reference waveform.

[0113] 6. Divide the reference waveform into multiple intervals using a range qualifier method. This range qualifier is calculated based on clinically significant range of ECG waveform data required for clinical decision making. Typically range qualifiers will create minimum of five distinct intervals in the entire data range. However multiple intervals more than five are also possible and present invention includes all possible intervals as required. Consider any target waveform which is to be shown as the training waveform on the display monitor (24). This waveform may be in a digital coordinates format plotted in any graphical format. This waveform is to be created from the reference waveform described above. To achieve this, use the same range qualifier method described above and divide the target waveform into similar intervals as done on the reference waveform. Compare each interval of the target waveform with that of the reference waveform. Here it is to be noted that comparison method in the present invention is valid irrespective of the scale of the two waveforms being compared. Present invention also includes all methods which can be implemented to bring both waveforms to a common scale before comparison. However, this step can also be eliminated as the comparison method is aimed at comparing the waveform morphologies only. Another embodiment of this involves, dividing each interval, mentioned above in step 8, into discrete points. The number of discrete points is estimated based on the resolution necessary to compare the two waveforms (reference waveform, target waveform). Once the discrete points are established for both waveforms, two points (one from each waveform) are extracted for comparison. Next, an algorithm is run as follows. A difference between the abscissa and ordinates of the points is calculated. As the difference between the abscissa tends to zero, the ratio between the difference in ordinates to the difference in the abscissa tends to be a very large number. In this case, an arithmetic subtraction of the ordinates is computed and stored in a memory array. This value may be a positive or negative integer or float value. Based on these computed values for each point, the reference waveform is compensated by a value equal to the computed value such that it matches the target waveform.

[0114] 10. Another embodiment of this technique involves dividing each interval, mentioned above in step 8, into discrete points. The number of discrete points is estimated based on the resolution necessary to compare the two waveforms (reference waveform, target waveform). Then, for each of the waveforms (reference waveform, target waveform), two consecutive points (X, Y) are taken and a first order derivative is calculated. Next, that value is doubly integrated. Finally, a second order derivative of the resultant value is computed. This is done for all the discretised points of both waveforms and stored in an array. Thus, two arrays (reference array, target array) are obtained. Then each value from both arrays (reference array, target array) is taken for comparison. The arithmetic difference between the last computed value gives an estimate of the deviation of the reference waveform from the target waveform. This deviation is then used to adjust the reference waveform to match the target waveform.

[0115] The waveform conversion module (14) in this invention uniquely applies a two- layer computational transformation process:

[0116] Layer 1: Analog-to-Digital Transformation with Clinical Scaling

[0117] • Analog ECG signals (typical range: ±2.5 mV) are digitized using a high- precision ADC with a pre-defined resolution, providing granular voltage readings.

[0118] • The digital signal is then scaled to match the pixel coordinate system of the display, ensuring morphological fidelity irrespective of the original amplitude. This step uses a dynamic scaling factor calculated based on the maximum and minimum signal amplitudes within a clinically relevant window (e.g., 10-second ECG strip).

[0119] Layer 2: Interval-Based Morphological Mapping

[0120] • The scaled waveform is segmented into clinically significant intervals based on cardiac physiology; typically dividing the waveform at points corresponding to the P-wave, QRS complex, ST segment, and T-wave, with additional division possible for high-resolution mapping.

[0121] • For each interval, a range qualifier is calculated to represent signal variance, slope, and curvature. This differs fundamentally from simple amplitude mapping used in prior arts.

[0122] This waveform conversion module (14) is important because, the interval segmentation aligns with cardiac electrophysiological components (P-wave, QRS complex, ST-segment, T-wave). This mirrors clinical ECG interpretation; not arbitrary segmentation. The pointwise compensation algorithm allows precise amplitude and time alignment between reference and target waveforms. The derivative-integral deviation algorithm allows morphology transformation accounting for slope, curvature, and rhythm-specific behaviours; making it clinically meaningful. This enables the creation of synthetic but clinically valid ECG signals.

[0123] In at least an embodiment, a selector (22) is configured to select a target waveform, representing a desired ECG morphology to be shown on the display monitor (24) and wherein the same segmenter (20) is configured to partition the target waveform into the same number and type of clinically significant intervals as the reference waveform. In at least an embodiment, a defibrillator simulation interface (30) coupled to the waveform conversion and scaling module (14), the defibrillator simulation interface (30) configured to replicate user interface functions of a clinical defibrillator without any high-voltage generation or delivery circuitry; and wherein the defibrillator trainer system (100) is configured to perform ECG waveform acquisition, scaling, interval-based segmentation, waveform transformation based on deviation between the reference waveform and the target waveform, artifact embedding, and simulated defibrillation, wherein the reference waveform is transformed to match the target waveform without delivering physical electric shock. Based on the achieved target waveform, a user can take a decision to deliver a shock, the said shock being a simulated shock without actually delivering a high voltage. In preferred embodiment, the defibrillator simulation interface (30) is coupled to a waveform conversion and scaling module capable of delivering a simulated shock without the associated high voltages and ability to revert the post shock ECG rhythm back to normal sinus rhythm- the control of such function provided to the user through a remote controlled device. Typically, the a defibrillator simulation interface (30) is coupled to the waveform conversion and scaling module (14) is also coupled to a transcutaneous pacing simulation module that provides simulation of transcutaneous pacing based on the ECG waveform.

[0124] In at least an embodiment, a biomarker database is configured to store various biomarker signals such as SPO2, ETCO2, NIBP or other biomarkers. Preferably, this biomarker database provides synchronized simulation of secondary physiological signals (SPO2, ETCO2, NIBP, and the like signals); enhancing training realism beyond pure ECG-based scenarios. Typically, the biomarker database is coupled with the display mechanism (24) in order to display the ECG signals with these biomarkers. This enables users to be trained in a variety of real- world scenarios such that users can correlate ECG rhythms with a variety of biomarkers on a single display mechanism (24) and system.

[0125] In at least an embodiment, a switching mode allows a user to toggle between training mode and examination mode such that users can train on the device systemin training mode and users can test themselves on the device system in examination mode.

[0126] This configuration ensures that ECG rhythms can be either directly downloaded and fed to the defibrillator system, of this invention, or real patient ECG rhythms can also be read directly by the invention’s defibrillator. Both of these inputs are only possible on the current invention's defibrillator circuits as the input is configured to accept both formats. None of the prior art defibrillators or ECG simulator boxes are capable of accepting both types of ECG rhythm formats (analog and digital).

[0127] One reason this was not possible on prior art simulators is that prior arts take only digitized ECG rhythm data as input and give analog data as output. This is achieved by a DAC (Digital-to- Analog-converter) module. Real patient ECG data is analog in nature (meaning data in mV). This requires an Analog to digital conversion built-in which the current invention has and prior art simulators do not have. Also, without using a DAC module, the invention's system is able to read and edit digital waveform data using conversion and scaling techniques to reproduce it on the display for the user.

[0128] Prior art simulators consist of real defibrillators with high voltage components and control circuits as one of the elements used for training and practice of medical students. However, in at least an embodiment, of the current invention, the high voltage components and its firing circuits have been eliminated but control circuits are maintained to create simulated shock scenario. In addition, all the hardware and software required to generate ECG rhythms is moved to be within the defibrillator enclosure itself. This unique construction has made the device extra-safe for usage and also allows generation and display of ECG signals, internally. All this was not possible in prior art solutions since real defibrillators can only accept bio-signals from external bodies or entities. With the presence of high voltage components, switches, and the associated control logic, creation of a simulated shock was not possible, in prior arts, due to risks associated with delivery of real shock which cannot be eliminated entirely. Therefore, this invention’s construction, as described above, is required.

[0129] When compared with the aforementioned first type of prior art simulators, the current invention is advantageous for at least the following reasons:

[0130] - The current invention’ s dumb-proof defibrillator trainer system is less bulky due to combining of the ECG rhythm box and defibrillator into one single device.

[0131] - The current invention’ s dumb-proof defibrillator trainer system also does not need multiple cables that are required to be connected between the three subsystems (i.e. box, defibrillator, and mannequin) of prior art.

[0132] - Since a real defibrillator is not required anymore, the present invention is completely safe for use compared to prior arts which involves high voltages during training.

[0133] - Moreover, all the prior art solutions are static in nature which means they all have fixed pre-programmed rhythms only which cannot be changed; whereas, the present invention makes the system flexible as per the features described above.

[0134] When compared with the aforementioned second type of prior art simulators, the current invention is advantageous for at least the following reasons:

[0135] - Here the problem of not getting to work with a real device is completely eliminated by the present invention. Also, the tablet PC based simulator cannot take inputs from real patients like the present invention can.

[0136] When compared with prior arts,

[0137] • Most prior arts (EP0813892A2, CN106683517A) either simulate with real defibrillators (high-voltage circuits) or are safety-limited versions of real defibrillators; whereas the current invention eliminates high-voltage circuitry.

[0138] • Prior arts like US2011213262A1 and IT202100030923A1 cannot ingest real-time analog ECG bio- signals directly from patient leads or external simulators; whereas, the current invention allows for signal ingestion pipeline comprising simulated signals and real-world signals.

[0139] • None of the cited prior art implement waveform segmentation based on clinical features (P-wave, QRS, T-wave) combined with pointwise compensation and derivative-integral deviation algorithms to reconstruct a target waveform from a reference.

[0140] • Prior arts do not support injecting real-world noise (AC line, baseline wander, motion, lead-off), meaning training lacks clinical realism.

[0141] • Systems like US11138905B2 or US2015194066A1 lack synchronized simulation of other vitals like SpCh, ETCO2, and NIBP alongside ECG. Many prior arts (US10580324B2, US6969259B2) are software-only or prompt-driven AED trainers, offering no analog signal interaction or real waveform processing.

[0142] • The current invention is the only system capable of ingesting real-time analog ECG signals via an integrated ADC pipeline, whereas prior arts are restricted to digital waveform libraries or DAC-based signal output.

[0143] • The elimination of high-voltage components in the defibrillator simulation while maintaining full operational workflow drastically improves safety compared to prior arts like EP0813892A2 or CN106683517A.

[0144] • The waveform conversion and scaling module (14) with dual-algorithm waveform morphing (pointwise compensation + derivative-integral deviation) is technically unmatched in prior art, which only offer static waveform display with no morphological transformation.

[0145] • The trainer is universally applicable to any manikin, conductive dummy, or even live humans without risk, owing to its zero-high-voltage architecture, which no prior system supports.

[0146] The TECHNICAL ADVANCEMENT, of this invention, lies in dumb-proof defibrillator trainer system which enables simulated shocks without requiring biosignals from external bodies or entities. This has been achieved by way of this invention’s inventive waveform conversion and scaling module which is configured to convert analog ECG data to a reference waveform and, then, use that reference waveform to achieve a target waveform correlative to simulated shocks required without the need for real body signals. Essentially, the present invention eliminates inherent safety risks associated with high-voltage circuitry while also eliminates the need for a smart manikin which is a general requirement of prior arts. Prior arts treat ECG waveform as a static output; this invention treats waveforms as dynamically transformable data objects, subject to signal processing manipulation and real-time conversion. The inclusion of both ADC and DAC pathways in the system is a fundamental shift from the unidirectional DAC-only approach of prior arts.

[0147] While this detailed description has disclosed certain specific embodiments for illustrative purposes, various modifications will be apparent to those skilled in the art which do not constitute departures from the spirit and scope of the invention as defined in the following claims, and it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the invention and not as a limitation.

Claims

CLAIMS,1. A dumb-proof defibrillator trainer system (100) comprising:• an ECG reader (12) configured to receive both analog ECG signals from an external bio-signal source and digital ECG waveform data from an ECG database, wherein the analog ECG signals or the digital ECG waveform data are processed to generate a reference waveform representing an initial ECG signal;• a waveform conversion and scaling module (14) coupled to said ECG reader (12), the said conversion and scaling module (14) comprising: o an analog-to-digital converter (ADC) (16) for digitizing received analog ECG signals to produce the reference waveform in a digital scale; o a scaling engine (18) configured to map the reference waveform to a pixel coordinate system of a display monitor (24), wherein the scaling engine (18) calculates a dynamic scaling factor based on maximum and minimum signal amplitudes within a clinically relevant time window to ensure morphological fidelity of the waveform irrespective of its original amplitude; o a segmenter (20) configured to partition the reference waveform into multiple clinically significant waveform intervals based on cardiac morphological features, using a range qualifier correlative to these physiological components; o a selector (22) configured to select a target waveform, representing a desired ECG morphology to be shown on the display monitor (24) and wherein the same segmenter (20) is configured to partition the targetwaveform into the same number and type of clinically significant intervals as the reference waveform o a comparator subsystem (26) configured to execute a waveform morphing operation by comparing each interval of the reference waveform to a target waveform, wherein said comparator (26) employs:■ a pointwise compensation algorithm (26a) configured to compute amplitude deviations at discrete sampling points within each interval and apply compensatory adjustments to the reference waveform, and / or■ a derivative-integral deviation algorithm (26b) configured to compute waveform deviation functions based on slope, curvature, and signal envelope characteristics between the reference waveform and the target waveform, to enable morphology transformation; and• a signal embedding module (28) communicably coupled to the waveform conversion and scaling module (14), the signal embedding module (28) configured to inject real- world signal artifacts into the reference waveform or the target waveform;• a defibrillator simulation interface (30) coupled to the waveform conversion and scaling module (14), the defibrillator simulation interface (30) configured to replicate user interface functions of a clinical defibrillator without any high-voltage generation or delivery circuitry; and wherein the defibrillator trainer system (100) is configured to perform ECG waveform acquisition, scaling, interval-based segmentation, waveform transformation based on deviation between the reference waveform and the target waveform, artifact embedding, and simulateddefibrillation, wherein the reference waveform is transformed to match the target waveform without delivering physical electric shock.

2. The system as claimed in claim 1 , wherein the comparator subsystem (26) includes a dual-mode computation engine configured to selectively operate between the pointwise compensation algorithm (26a) and the derivative-integral deviation algorithm (26b) based on waveform complexity metrics.

3. The system as claimed in claim 1, wherein said system comprising a biomarker simulation module configured to generate and synchronize physiological signals and biomarkers with the ECG waveform, for multimodal clinical scenario simulation, said biomarker simulation module dynamically correlates ECG waveform morphological changes to synchronized changes in at least one biomarker.

4. The system as claimed in claim 1, wherein said waveform morphing process comprises adjusting waveform intervals through adaptive scaling based on slope analysis and curvature deviation thresholds.

5. The defibrillator trainer system as claimed in claim 1, wherein the ECG reader (12) is configured to ingest analog ECG signals from an external bio-signal source comprising an actual patient.

6. The defibrillator trainer system as claimed in claim 1 , wherein the ECG reader (12) is further configured to ingest digital ECG waveform data from an ECG database.

7. The defibrillator trainer system as claimed in claim 1, wherein the ECG reader (12) comprises a signal selector configured to toggle between ingestion of analog ECG signals and digital ECG waveform data.

8. The defibrillator trainer system as claimed in claim 1, wherein the analog-to- digital converter (16) is further configured to convert analog ECG data from an ECG database into a digital scale for waveform processing.

9. The defibrillator trainer system as claimed in claim 1, wherein the segmenter (20) is further configured to partition the reference waveform into a minimum of five distinct intervals using a range qualifier correlated to clinically significant ranges of ECG waveform data.

10. The defibrillator trainer system as claimed in claim 1, wherein the comparator subsystem (26) is further configured to compare each interval of a target waveform with a corresponding interval of the reference waveform, irrespective of amplitude scaling between the two waveforms.

11. The defibrillator trainer system as claimed in claim 1, wherein the scaling engine (18) is further configured to apply a common scaling factor to both the reference waveform and the target waveform prior to interval-wise comparison by the comparator subsystem (26).

12. The defibrillator trainer system as claimed in claim 1, wherein the comparator subsystem (26) is configured to compute deviation between the reference waveform and the target waveform based on comparison of corresponding intervals.

13. The defibrillator trainer system as claimed in claim 1, wherein the comparator subsystem (26) comprises a divider module configured to subdivide each clinically significant waveform interval into a plurality of discrete sampling points based on a resolution parameter determined by a desired waveform comparison fidelity.

14. The defibrillator trainer system as claimed in claim 1, wherein the comparator subsystem (26) further comprises a second comparator configured to compute amplitude differences between corresponding discrete sampling points of a reference waveform and a target waveform.

15. The defibrillator trainer system as claimed in claim 1, wherein the comparator subsystem (26) further comprises a first computation module configured to generate a compensation matrix based on the amplitude differences computed by the second comparator.

16. The defibrillator trainer system as claimed in claim 1, wherein the compensation matrix is configured to algebraically adjust the reference waveform pointwise to conform to the target waveform.

17. The defibrillator trainer system as claimed in claim 1, wherein the pointwise compensation algorithm (26a) executed by the comparator subsystem (26) is configured to compute amplitude deviations at each discrete sampling point within each interval and apply the corresponding compensation to the reference waveform while preserving signal integrity.

18. The defibrillator trainer system as claimed in claim 1, wherein the pointwise compensation algorithm (26a) is configured to enable precise morphologicalrecreation of clinically significant target rhythms including but not limited to multifocal atrial tachycardia and hyperkalemia-induced sine wave patterns.

19. The defibrillator trainer system as claimed in claim 1, wherein the comparator subsystem (26) comprises a divider module configured to subdivide each clinically significant waveform interval into a plurality of discrete sampling points based on a resolution parameter determined by waveform comparison requirements.

20. The defibrillator trainer system as claimed in claim 1, wherein the comparator subsystem (26) further comprises a second computation module configured to compute first-order derivatives between each pair of consecutive discrete sampling points of the reference waveform and the target waveform.

21. The defibrillator trainer system as claimed in claim 1, wherein the second computation module is further configured to compute a double integral of the first-order derivatives to reconstruct signal envelope deviations representing macro-morphological differences between the reference waveform and the target waveform.

22. The defibrillator trainer system as claimed in claim 1, wherein the second computation module is configured to generate a reference array and a target array representing derivative and integral computations of the reference waveform and the target waveform respectively.

23. The defibrillator trainer system as claimed in claim 1, wherein the comparator subsystem (26) further comprises a third comparator configured to perform anelement-wise comparison of the reference array and the target array to compute deviation values for each discrete sampling point.

24. The defibrillator trainer system as claimed in claim 1, wherein the deviation values computed by the third comparator are configured to drive waveform morphing operations such that the reference waveform is transformed to match the target waveform in terms of slope, curvature, and fidelity.

25. The defibrillator trainer system as claimed in claim 1, wherein the waveform conversion and scaling module (14) comprises a first computational layer configured to perform analog-to-digital conversion of analog ECG signals within a voltage range of ±2.5 millivolts using a high-precision analog-to- digital converter (ADC) with a predefined resolution.

26. The defibrillator trainer system as claimed in claim 1, wherein the waveform conversion and scaling module (14) comprises a second computational layer configured to segment the scaled waveform into multiple clinically significant waveform intervals including a P-wave, QRS complex, ST-segment, and T- wave.

27. The defibrillator trainer system as claimed in claim 1, wherein the second computational layer is configured to calculate a range qualifier for each waveform interval, the range qualifier representing signal variance, slope, and curvature within that interval.

28. The defibrillator trainer system as claimed in claim 1, wherein the segmenter (20) of the waveform conversion and scaling module (14) is configured toperform interval segmentation aligned with cardiac electrophysiological components comprising a P-wave, QRS complex, ST-segment, and T-wave.

29. The defibrillator trainer system as claimed in claim 1, wherein the pointwise compensation algorithm (26a) executed by the comparator subsystem (26) is configured to enable precise amplitude alignment and time alignment between a reference waveform and a target waveform.

30. The defibrillator trainer system as claimed in claim 1, wherein said defibrillator trainer system comprises a defibrillator simulation interface (30) coupled to the waveform conversion and scaling module (14) as also being coupled to a transcutaneous pacing simulation module that provides simulation of transcutaneous pacing based on the target waveform.

31. A method for operating a dumb-proof defibrillator trainer system, the method comprising:• acquiring an ECG signal from either (i) an analog bio-signal input via an analog-to-digital converter (ADC) (16) or (ii) a digital ECG waveform from an ECG database or an ECG simulator device having preprogrammed rhythms;• converting the acquired ECG signal into a reference waveform, wherein the reference waveform represents an initial ECG signal derived from either the analog ECG input or the digital waveform, and wherein the converting step comprises signal digitization and amplitude-to-pixel scaling using a dynamic scaling factor calculated based on maximum and minimum signal amplitudes within a clinically relevant time window;• selecting a target waveform representing a desired ECG morphology to be displayed on a graphical user interface, wherein the target waveform is retrieved from the ECG database;• segmenting both the reference waveform and the target waveform into a plurality of clinically significant waveform intervals based on cardiac morphological features, using a range qualifier correlative to said morphological features;• applying a waveform morphing process comprising: o calculating pointwise amplitude deviations between corresponding discrete sampling points of the reference waveform and the target waveform within each interval, and applying compensatory adjustments to the reference waveform; o computing deviation functions based on slope, curvature, and signal envelope characteristics by performing a derivative-integral deviation process, including calculating first-order derivatives between consecutive points, double integrals for envelope reconstruction, and second-order derivatives for curvature fidelity; o generating a compensated waveform by transforming the reference waveform to match the morphology of the target waveform while preserving amplitude integrity and morphological fidelity;• embedding simulated signal artifacts, including AC line noise, baseline wander, motion-induced artifacts, and lead detachment artifacts, into the compensated waveform;• displaying the compensated waveform on the graphical user interface, wherein the display further overlays biomarker indicators; and• activating a simulated defibrillator interface to enable user interaction with virtual controls for energy selection, charge, and discharge, without generation or delivery of high-voltage electrical output.

Images