HippoScreen Promotes the Diagnosis of Mental Health. HippoScreen created the Stress EEG Assessment system with AI analytics technologies to enhance mental health diagnosis.
Maximizing AI for a Better Mental Depression Diagnosis
The SEA system from HippoScreen was created to address these issues. The SEA method aids medical professionals in making more precise diagnoses of mental health issues. HippoScreen employs a novel strategy by utilising brainwave technology, in contrast to conventional techniques that only depend on patients’ self-evaluation. This method makes the most of real-time behavior processing to determine the user’s cognitive state by analyzing their brainwaves.
The SEA system consists of an AI algorithm for data analysis, a GUI (Graphical User Interface) for test process management, and an EEG amplifier for data collection and signal processing. Through the analysis of 90-second brainwave signal segments, SEA generates a numerical assessment index that quantifiably and objectively depicts the probability of a person suffering depression.
By optimizing different algorithms used in data preparation, feature extraction, feature selection, and classifiers, the solution is intended to produce the intended result. In order to accomplish this, HippoScreen had to overcome a few obstacles that other AI healthcare solutions also confront. The main challenges included making sure the AI model could handle data variation, which is essential for successful results, designing a well-controlled test procedure to maintain the data quality, and generalizing the model to ensure its applicability in actual clinical usages.
Because of this, it may take days to find the ideal feature set and the ideal parameters among different algorithm combinations. Improving the performance of these algorithms is essential in this situation and may hold the secret to delivering these optimized outcomes on time.
Intel was instrumental in helping HippoScreen increase the effectiveness and build times of the deep-learning models it employed in its brainwave AI-based SEA system.
Improving Algorithm Efficiency and Diagnostic Accuracy with Intel
Using the Intel AI Analytics Toolkit and Intel oneAPI Base Toolkit, HippoScreen was able to optimize its SEA solution and enhance the effectiveness, performance, and precision of their deep learning models while cutting down on the turnaround times for important diagnostic reports.
To produce a distinct decision factor, HippoScreen’s development method combines a variety of models and algorithms. Using HippoScreen’s patented algorithms, which were created especially for their particular requirements, is essential to this procedure. HippoScreen incorporates the deep learning models of Intel-optimized PyTorch in addition to their own methods. This covers models like Shallow ConvNet, EEGNet, and SCCNet. Large volumes of data can be processed by these advanced models, which can also spot patterns that conventional analytic techniques might miss.
Traditional machine learning methods from Intel’s scikit-learn are also included to further improve the system’s capabilities. These algorithms offer reliable and tested methods for data analysis, and they include Kmeans, Support Vector Classification, and Support Vector Regression. The aspects of EEG data are analyzed using this wide range of models and methods.
A more thorough and nuanced study is made possible by the combination of these diverse methodologies, which eventually results in the creation of a special decision factor. This decision factor is the result of HippoScreen’s creative approach to mental health diagnosis and was created by combining proprietary algorithms, deep learning models, and conventional machine learning techniques. This method has the potential to improve treatment results for countless people by delivering faster and more precise diagnoses.
The Intel oneAPI Math Kernel Library (oneMKL) is another essential technology that has transformed HippoScreen’s deep learning procedure. Specifically created for machine learning applications, this library offers highly optimized mathematical functions. HippoScreen models can execute intricate mathematical computations more quickly with oneMKL. This allows HippoScreen to create deeper learning architectures that are larger and more accurate while also saving significant processing time.
HippoScreen has been able to leverage industry-leading libraries and pretrained models through the use of frameworks such as Intel Extension for TensorFlow and PyTorch Optimizations from Intel. With high-level APIs, copious documentation, and a sizable developer community actively working to enhance them, these frameworks offer a strong basis for creating sophisticated AI algorithms.
In addition to expediting the development process, using these well-known frameworks guaranteed compatibility with a variety of hardware setups, which made it simple for HippoScreen to incorporate its AI solutions into a variety of settings. Additionally, HippoScreen has a competitive edge due to its predictive analytics skills, which allow them to foresee the requirements and behaviors of their customers.
Delivering the Performance Boost for Timely and More Efficient Depression Diagnosis
By leveraging the capability of Intel CPUs for AI workloads and the Intel AI Analytics Toolkit and Intel oneAPI Base Toolkit, HippoScreen’s SEA was able to achieve a 2.4x gain in performance. This was crucial in helping HippoScreen’s SEA reach the ideal performance threshold needed to boost productivity and drastically cut down on diagnosis time.
All things considered, Intel’s AI frameworks and tools have been crucial in enabling HippoScreen’s SEA to fully utilize its advanced analytics capabilities. HippoScreen used Intel VTune Profiler to obtain detailed information about the number of software threads and total logical CPUs in both their own and Intel’s Python environment using various OpenMP modules. Understanding the system’s functioning and pinpointing areas for improvement were made possible by this in-depth examination.
Intel VTune Profiler suggested lowering the thread count in both the Intel and HippoScreen Python environments. This suggestion was made in light of the fact that both instances featured thread oversubscription, which is the wasteful use of CPUs caused when more software threads are assigned than there are logical CPUs. HippoScreen could balance performance and CPU usage by implementing the suggestions and modifying the thread count.
In order to determine the ideal thread count the “sweet spot” where performance was maximized and CPU utilization was minimized this tuning procedure required meticulous testing and analysis, yielding a twofold performance improvement.
A major accomplishment was striking this equilibrium, which enabled HippoScreen to maximise system performance without putting undue strain on the CPU. By avoiding needless stress on the CPU, this not only increased the system’s efficiency but also might have increased the hardware’s lifespan.
Delivering Real-World Benefits
HippoScreen’s SEA has benefited greatly from successfully utilising Intel AI tools and technology, which represents a significant improvement in its overall performance and operational capabilities. The improved capacity to examine brainwave patterns and comprehend different cognitive states is one of the main advantages. Researchers can now better understand intricate neural processes like attention, memory, and emotions by utilising AI. Intel AI tools and technologies’ quick processing speeds allow for more effective analysis of large volumes of data, which advances the knowledge of how various elements affect brain function.
Additionally, this integration has enhanced research capacities, creating intriguing opportunities for real-world applications in domains like as education and healthcare. Medical practitioners can use this advanced analytics platform, for example, to more accurately diagnose cognitive impairments or to monitor patients’ progress throughout treatments. Accurate diagnosis and progress tracking can result in more successful treatment regimens and possibly better patient outcomes.
These fresh perspectives can be applied in the field of education to create more individualized learning programs that better meet the needs of each student. Teachers can modify their teaching strategies and tactics to better fit each student by knowing how various elements affect cognitive function. This could result in better learning outcomes.
In conclusion
An important turning point in neurology diagnosis has been reached with the integration of Intel AI algorithms into HippoScreen’s SEA. The partnership between Intel and HippoScreen AI shows how cutting-edge technology can improve the capacity to efficiently monitor brainwaves, which is an exciting development in neurology. Researchers, clinicians, and patients gain from increased screening accuracy, faster turnaround times, and better patient care through individualized early intervention strategies by utilising the power and intelligence provided by platforms such as scikit-learn and Intel Extension for PyTorch.