Rehabilitation through Brain Machine Interfaces

Stephen Hawking: Former Lucasian Professor of Mathematics at the University of Cambridge, world renowned theoretical physicist, diagnosed with Amyotrophic Lateral Sclerosis (ALS) at the age of 21

Christopher Reeves: American film actor, fondly remembered for his motion-picture portrayal of the fictional superhero Superman; suffered a spinal cord injury and became a quadriplegic at the age of 43

These are probably some of the first names which pop up when we think of people living with disability and the need for rehabilitation technologies. A severe form of disability arises because of limb amputations as an after effect of traumatic accidents, degenerative diseases or victims of a social disorder.

These conditions often mean drastic lifestyle changes for the disabled, worsened with limited means of livelihood, social disconnect and dependability. Restoring mobility for such patients is a goal of many research teams around the globe, most focusing on repairing the damaged nerves and trying to find ways for nerve signals to bypass the injury site. Another approach is to build prosthetic devices which are essentially closed-loop architectures based on biofeedback (EMG, EEG etc) signals.

Brain Machine Interface (BMI) is a recent technological advancement which taps the brain waves using surface EEG electrodes which are subsequently used to control a prosthetic device. The guiding premise in the design of such interfaces is the following:

The two activities of actually moving a real arm and just thinking about moving the arm produce same neuronal firing in the brain.

A smart wheelchair based on this premise could be a system having an EEG cap to non-invasively acquire the signals from the patient’s primary motor cortex area in the brain, and deploy a real-time pattern recognition algorithm to identify the mental states of the patient – whether he is thinking ‘left’, or ‘right’ or is idle, and subsequently control a wheelchair based on these inputs. The EEG cap however, as opposed to implanted neurochips has a limited bandwidth but at the same time is free from complications because of surgical procedures.

In an attempt to develop prosthetic technology capable of restoring motor control and tactile feedback to spinal cord injury patients, an interesting experimental study was reported in Nature where researchers from Duke University Center for Neuroengineering had successfully trained two monkeys to use the electrical activity in their motor cortex to control the arm of an onscreen avatar without physically moving themselves, hinting at the possibilities of wearable thought – controlled prosthetic devices in the near future.

A recent case report from Cornell University represents the first successful demonstration of a BCI-controlled lower extremity prosthesis for independent ambulation, which might allow for a cheap, easy, and non-invasive option to getting paraplegics walking again. A wireless brain-machine interface developed by Neural Signals uses implantable electrodes to address Locked-In syndrome for jaw movements, a terrifying brain lesion which leaves patients aware but almost entirely without the power to move.

However fascinating these new developments seem to be, they are far from a commercial reality. The non invasive systems suffer from poor spatial and temporal resolutions, driving the quest to devise more powerful and usable brain recording devices. The medical community would benefit for sure, but possibilities are limitless – gaming, cursor control, brain timing and brain-to-brain communication to name a few. And who knows, it might also open an unethical dimension of hacking the brain!

The right product for emerging markets?

The last decade has witnessed a rapid shift of focus of large multinationals towards emerging markets, across diverse industrial domains. Apart from the obvious benefits of capitalizing on the new opportunities, much of this paradigm change has been driven by the need of fitting sustainability in their business models. The medical technology industry has joined the wagon as well, primarily driven by some prominent growth drivers in the emerging economies – changing medical technology landscape, improving healthcare delivery and financing and changing patient profiles with increased life expectancy.

The medical technology industry in these markets is extremely aggressive and split, with domestic firms mainly manufacturing low technology products and multinationals primarily importing high-end medical equipment. However, a new breed of home-grown mid-market innovators cognizant of the local needs, are shaking up the global competitive landscape with low-price, medium quality products whereas global giants have evolved from the distributer based business mindsets to setting up local manufacturing units. But because of insufficient knowledge of the clinical needs and usage patterns of consumers in the emerging markets, appended with the local competition barely visible to a multinational company headquartered in the U.S. or Europe, writing the requirement specification for the right kind of product is often a challenging task. GE Healthcare’s MAC 400 value-segment electrocardiography machine is a remarkable example of the right product which had the potential of converting a local community need into a viable and scalable business. Let us take a closer look at some of the salient features which could possibly define such a value segment product:

[Acceptable Quality] It does what it is destined to do, and does it well. The underlying technology allows the end-user to carry out its intended clinical use.

[Affordable] It is affordable by the ‘emerging market customer’.

[Appropriate] It serves a need and is very useful. It can be a basic product platform ripped off some of its premium features in response to the local needs. The usefulness indirectly infers that the product must be reliable and durable.

[Well-positioned] It is competitive but not an internal competitor. There should be clear lines of demarcation between the premium products and the value segment products so that the latter does not cannibalize the former.

[Innovative] It is nimble, evolving and innovative. The pressures of cost, pace and quality compel everyone to explore solutions outside conventional wisdom, which brings in the power of innovation. At its heyday, innovation can also become disruptive and give birth to a game-changing product altogether!

In the Indian context, it would be worthwhile to take a quick look at some of the home-grown value-segment product manufacturers, and learn their definitions of such products:

Company Product Segment Company Product Segment
 
Opto-Circuits Equipments, Interventional devices Sushrut Surgical Orthopedic Implants
Perfint Healthcare Soft tissue intervention Bigtec Labs Life Sciences
Poly Medicure Ltd Consumables SkanRay Diagnostic X-ray
Relisys Medical Stents, Catheters Trivitron Medical Cardiology, Imaging

The value-segment product for an emerging market is a paradigm, and perhaps can be better understood by analogies. The goal for such a product could then possibly be Mercedes-level quality and attractiveness, Toyota-level durability and margins, and Skoda-level prices.

The Big-Data in Healthcare: challenges & opportunities

The healthcare industry is witnessing an explosive growth in the volumes of digital medical data. Advances in digital imaging technologies and electronic patient record systems, combined with federally mandated data retention and retrieval policies are presenting healthcare IT professionals with a number of new challenges related to storing, managing and providing access to its medical data. According to IDC Health Insights’ 2010 EMR  and PACS  storage  survey, storage takes up a large percentage of  overall IT budget for providers, with a large portion of outpatient  centers (50%) and hospitals (57%) allocating more than 20% of their  IT budget to storage. One of the most compelling trends observed at the IBM’s Big Data Policy Event points to the fact that the amount of data generated per hospital will increase from 167 terabytes to 665 terabytes by 2015.

In the U.S., organizations that transmit an individual’s protected health information (PHI) across Internet applications or electronic systems are required to meet Health Insurance Portability  and  Accountability  Act  of  1996  (HIPAA)  requirements. In order to be compliant, healthcare IT solution providers must design their systems and applications to meet HIPAA’s privacy and security standards and related administrative, technical, and physical safeguards. Apart from confidentiality and robust access control protocols, the other hurdles in managing medical data lie in the following areas – long-term vendor viability, continuity of care through backup & disaster recovery solutions, rapid scalability & secured migration, multi-site & enterprise wide collaboration and of course affordability in terms of real-estate and infrastructural requirements.

Cloud computing holds the promises of reduced costs, pay-as-you-go services, and improved agility, allowing organizations to leverage external IT capabilities that they may not have in-house. However when it comes to medical data the top concerns for health IT administrators are security and availability, which could be mitigated through properly architected cloud frameworks. These increased burdens augmented with higher sensitivity in handling medical data have led to the development of specialized solutions tailored for the healthcare industry, by the leading service providers in this sphere – EMC, NetAPP, Amazon, HP, Hitachi, Intel, IBM etc. An additional area of concern in the context of healthcare industry is the rapidly evolving medical technology landscape. A close example would be in the field of personalized medicine, – where the next-generation genome sequencing technologies are rapidly churning out terabytes of data during standard gene annotation experiments, clearly signaling that we are only at the beginning of healthcare’s digital information explosion.

Despite the burden associated with the enormity of these datasets, the abundance of clinical data holds the potential of changing the course of healthcare as well. With advanced data mining techniques, large chunks of data can be leveraged for the identification of disease patterns, discovery of new drugs, optimization of methods of clinical care, and efficient management of patient flow. On the other end of the spectrum are initiatives such as Global Viral Forecasting (GVF) which are continually data hungry as they harness big data to prevent global pandemics before they start!