Our ‘Mathematical Microscope’ Toolkit
Major biomedical signals encountered in basic and medical research are produced by nonlinear molecular and cellular ensembles and exhibit non-stationary (‘chaotic’) behaviour. However, almost all current signal processing methods used to analyse biological signals use linear stationary techniques, supported by heuristic rather than mathematically rigorous methods. One common approach used is averaging, which hides fine elements of the raw waveform’s morphology.
Our ‘chaos theory’ based mathematical microscope resolves this problem, allowing higher resolution analysis of all non-stationary biological signals, both in terms of frequency and time resolution.
Our Kaoskey research and product development has initially been focused on applications using EEG recordings and ECG recordings.
Kaoskey continues to develop unique and innovative approaches to the analysis of non-stationary biomedical signals, such as EEG. Our novel approach provides the means for comprehensive real-time extraction of significant features from EEG recordings not accessible using conventional linear mathematics techniques.
This allows us better understanding of the basic mechanisms of brain activity, effective diagnosis of brain disorders, and enabling the design of effective real time brain-computer interfaces (BCI).
Kaoskey’s recent focus has been on detection of epileptic seizures from human and animal EEG recordings. Software has been designed and developed for rapid real-time analysis of the EEG, as well as for off-line analysis of archived long-term EEG recordings.
Our prototype algorithm and software that is based on KaosKey’s novel deterministic chaos-based modelling (DCBM) approach and KaosKey’s universal real-time technique for pattern recognition in signal model (PRISM) has proven to be very effective in early seizure onset detection.
Our first EEG product – ASSYST – is a powerful pre-clinical research tool for rapidly detecting and assessing the epileptogenic activity in rodent EEG recordings – seizures, HFOs, SWDs, inter-ictal spikes, etc. ASSYST V3.0, with greatly enhanced SWD assessment capability requested by our customers, has just launched in May 2020.
Our new ASSYST software for automated seizure detection in rodents detects epileptic seizures with <1% false negatives and <10% false positives with good EEG data, and it has an average processing time of 1 minute for 24hrs of 6 channels of continuous rodent EEG recording, a huge time saving versus conventional manual video-EEG seizure scoring methods.
We are currently in clinical validation stage with a new product for the detection of seizures using human ECG recordings for deployment in a wearable device for seizure detection, logging, and alerting (TRIO).
Given the diversity of epileptiform EEG abnormalities, the corresponding ECG abnormalities may take a variety of forms. In addition to conventional measurements of the heart rate and heart rate variability, Kaoskey’s development goal is to take into account the specific changes of the morphology of ECG waveforms linked to the seizure induction and development. Kaoskey’s advanced tools of mathematical microscope are well suited for these purposes.
- 11th Asian & Oceanian Epilepsy Congress, 13-16 May 2016, Hong Kong
A Novel Model-Based Method for Real-time Detection of Electrographic Seizures
- 32nd International Epilepsy Congress, 2-6 September 2017, Barcelona
An automated tool for reliable detection of seizures in rodent models of epilepsy
- Neuroscience 2017, November 11-15, Washington, DC
A novel user-friendly automated tool to accurately detect seizures in rodent models of acquired and genetic epilepsy
- 71st AES Annual Meeting, December 1-5 2017, Washington, DC
A universal automated tool for reliable detection of seizures in rodent models of acquired and genetic epilepsy
- 72nd AES Annual Meeting, November 30 – December 4 2018, New Orleans
Interactive automated software for reliable seizure detection in rat and mouse models of genetic and acquired epilepsies.
- 33rd International Epilepsy Congress, 22-26 June 2019, Bangkok
Automated interactive tool for reliable seizure detection in rat and mouse models of genetic and acquired epilepsies.
- Melkonian D, Blumenthal T, Barin E (2018) Quantum theory of mass potentials. PLoS ONE 13(7)
- Melkonian D, Korner A., Meares R., Bahramali H. (2012) Increasing sensitivity in the measurement of Heart Rate Variability: the method of Non-Stationary RR time-frequency analysis. Computer Methods and Programs in Biomedicine 108: 53-67.
- Melkonian D. (2010) Similar basis function algorithm for numerical estimation of Fourier integrals. Numerical algorithms, 54: 73-100.
- Casillas-Espinosa PM, Sargsyan A, Melkonian D, O’Brien TJ. (2019) A universal automated tool for reliable detection of seizures in rodent models of acquired and genetic epilepsy. Epilepsia. 60:783-791.